{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:50:19.384351",
     "start_time": "2017-07-22T10:50:17.856232"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import os\n",
    "os.chdir('..')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import trading.start\n",
    "import trading.portfolio as portfolio\n",
    "import config.settings\n",
    "from time import sleep\n",
    "from core.utility import *\n",
    "from trading.accountcurve import *\n",
    "import data.db_mongo as db\n",
    "import config.portfolios\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 10\n",
    "p = portfolio.Portfolio(instruments=config.portfolios.p_trade)\n",
    "i = p.instruments\n",
    "from trading.bootstrap import bootstrap\n",
    "from IPython.display import FileLink, FileLinks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How our System Works\n",
    "\n",
    "There are three main objects that are important to us:\n",
    "* Instrument\n",
    "* Portfolio\n",
    "* AccountCurve\n",
    "\n",
    "## Instrument\n",
    "\n",
    "An instrument in our system refers to a particular futures market, such as Corn or Gold.\n",
    "\n",
    "Instruments are defined in [config/instruments.py](file/../../config/instruments.py).\n",
    "\n",
    "When our system starts up, we instantiate an [Instrument](file/../../core/instrument.py) object for each instrument.\n",
    "\n",
    "The instrument object:\n",
    "* Holds all the properties and settings which are used by the system for calculation and rolling\n",
    "* Holds all the details which are used by Interactive Brokers for trading\n",
    "* Holds methods for:\n",
    "  * Calculating position\n",
    "  * Calculating panama prices\n",
    "  * Downloading historical data\n",
    "  * Bootstrapping weights for what rules work best on this instrument, based on the historical data in the system.\n",
    "  \n",
    "## Volatility or Standard Deviation\n",
    "\n",
    "There are many different ways of calculating volatility. In our simple case, we assume daily returns are Gaussian distributed, and we calculate volatility as being the standard deviation of daily returns. Under this assumption, volatility and standard deviation are the same thing.\n",
    "\n",
    "It is worth pointing out that the assumption of Gaussian returns is a dangerous one, and is only suitable for a simple system like this. There are plenty of examples when a single day move in prices has been several standard deviations.\n",
    "\n",
    "## Panama Prices\n",
    "\n",
    "We have a problem when we try to see trends on futures contracts. In order to apply our moving averages, we need a continuous series of prices. The contracts are seperate though, with different prices, which means there will be big jumps in the prices on the days we roll:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:50:21.414697",
     "start_time": "2017-07-22T10:50:19.386918"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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QQUp8hGdlrVNutxtutwehUBArVizD4sVLoOs6BEFIXO9BMBhAMBiEx1OTdr9AIACv1wuH\nw4nW1hbcd99K/Mu/LAUAjBs3HqNGjU57LIfDgdraWiiKgjVrVmHu3Hlwu919fg6srBERERERVZIK\nq6w1fvm6LqtghdLUdAzLl9+NefPm49JLL8f69T+xrguFgqipqYHH40EoFEq5PASv1wsA2Lv3E6xa\ntRxLl96GqVPP7fSxfD4fVq5chqlTz8WiRV/Ny/pZWSMiIiIiqiBmZU1XWVnrzIkTrbjzzm9iyZJb\nMGfONQCAiRMnYufO7QCAN998HVOmTMWECZOwa9c7iEajCAQCOHBgH0aPHoN9+z7FypXLsGrVGlx4\n4cxOHysajeD225fgqqvm4oYbvp6358DKGhERERFRJUlU1tgG2bnHHtsIv9+PTZs2YNOmDQCA1atX\nYdWq1Vi/fi1GjhyFWbMugSRJmD//OixduhiapuGmm26Gw+HA+vU/RSwWwyOP/AAAUFNTg4ce+lHO\nx3r22adx5MhhbN36DLZufQYAsHz5KgwbNrxPz0HQdV3v00/oo+ZmfykfPktjo7fs1kREhcf3PlH/\nw/c9VarjW36Dthf/ByO+uxrOESNLvZyKUo7v+8ZGb4fXsQ2SiIiIiKiA2l97Ffu/+x2ooWBefp41\nYIRtkFWPYY2IiIiIqICaNv4SsSOHEdi+PT8/0BwwolXGgBHqPYY1IiIiIqJCkoxwFT1yOC8/jgNG\n+g+GNSIiIiKiAnIMPxUAEMtXWLMGjLCyVu0Y1oiIiIiICkhKnNkVazqWnx8osrLWXzCsEREREREV\nkK4oAAA1kK8BI+ah2Axr1Y7nrBERERERFZAejxt/RiPQolGIDkfffmCissY2yM4pioIHH1yNo0eP\nIh6P4frrb8S0aZNx1113QxAEnH76GNx55zKIooitW5/Bc8/9FpIk4frrb8TMmZ9HOBzG6tXfgd/v\nhyzbsGLFPWhsHAwAUFUVq1b9K+bMuRbTp88AAKxd+wh27XoXqqpi7tx5mDt3Xp+fAytrREREREQF\nZFbWAEBpa+vx/X1vvo4D966CFosBAATJqLfoqtLZ3fq9F174A2pr67Fu3Qb88Ic/wY9+9H08+OCD\nWLx4Cdat2wBd1/Hqq6+gtbUFTz21BT/72S/xox/91DoM+3e/ewZnnDEBa9f+ApdddgU2b34MAHD4\n8CF885uL8eGHH1iPtXPndhw6dBDr12/EunUbsHnzr+Dz+fr8HFhZIyIiIiIqIF2JW18r7W2wDxnS\no/sHd7+P6GcHoPp9EAcOSjlnrTIqa6+/tBef7jme1595+pmDMePiMZ3eZvbsL2L27EsAALquQ5Jk\n7N69G/feey4AYPr0Gdi27S1IkojJk6fAbrfDbrdj+PDTsHfvx1iwYCHURKtpU9MxeBN7D0OhEJYt\nW4nNm39lPdakSZMxdux4AIAgCNA0DbLc96jFyhoRERERUQHp8WQFTO1FZU0LhwEAotMFILWyxj1r\nnXG73XC7PQiFglixYhkWL14CXdchCELieg+CwQCCwSA8npq0+wUCAQCAJEm49dZv4Omnn8A//MMs\nAMC4ceMxatTotMdyOByora2FoihYs2YV5s6dB7fb3efnwMoaEREREVEB9bUNUotEAACi02lcYA0Y\nqYw2yBkXj+myClYoTU3HsHz53Zg3bz4uvfRyrF//E+u6UCiImpoaeDwehEKhlMtDVhUNAH7840dx\n4MB+3H33bXjyyec6fCyfz4eVK5dh6tRzsWjRV/OyflbWiIiIiIgKyBwwAvSuGqaFwxDsdmsKpCBz\nGmR3nDjRijvv/CaWLLkFc+ZcAwCYOHEidu7cDgB4883XMWXKVEyYMAm7dr2DaDSKQCCAAwf2YfTo\nMXj88Y344x9/DwBwuVwQzfPtcohGI7j99iW46qq5uOGGr+ftObCyRkRERERUQLoSh+O0ERj8lUVw\njBjZ4/trkQhEl8v63mqDVCqjslYqjz22EX6/H5s2bcCmTRsAAKtXr8KqVauxfv1ajBw5CrNmXQJJ\nkjB//nVYunQxNE3DTTfdDIfDgauumos1a+7B888/B03TsHz5dzt8rGeffRpHjhzG1q3PYOvWZwAA\ny5evwrBhw/v0HARd1/U+/YQ+am72l/LhszQ2estuTURUeHzvE/U/fN9TsXz8ja/DMWIERnTyYb8z\ne++6DaLTidH3fw8AEHhnJ46s/TEaF1yHAZdens+lVr1yfN83Nno7vI5tkEREREREBaLrOnRFgSDb\nev0ztHDYGi4CAILMASP9BcMaEREREVGBmK2KQi/HuOuqCj0WS2uDtAaMsA2y6jGsEREREREViDlc\nRLD1rrKWNQkSsAaNsLJW/RjWiIiIiIgKpK+VNS1inrGWEtZkDhjpLxjWiIiIiIgKRFcSlbVe7lmz\nDsROmwaZGCHPylrVY1gjIiIiIioQPZ6orNl6WVkLm22QOUb3M6xVvU5fNfF4HMuXL8fhw4cRi8Ww\nZMkSDBs2DKtWrYIkSRg1ahTuv/9+iKKIJ598Elu2bIEsy1iyZAlmz55drOdARERERFSW+lxZy9EG\naQ0YUdkGWe06DWtbt25FfX09Hn74YbS1teHaa6/FpEmTsHTpUnzhC1/AXXfdhZdffhmTJ0/G448/\njqeffhrRaBQLFy7EzJkzYbfbi/U8iIiIiIjKTrKy1ruwpoaCAADJ7bEu4+j+/qPTsHb55Zfjsssu\nA2CcESFJEiZMmIC2tjbouo5gMAhZlrFr1y5MnToVdrsddrsdI0aMwJ49e3D22WcX5UkQEREREZWj\nZGWtd22QaiAAAJBqaqzLBI7u7zc6fdV4PEaCDwQCuPXWW3H77bdDEATce++9+NnPfgav14sLLrgA\nf/zjH+H1etPuF0i8sLrS2YndpVKOayKiwuN7n6j/4fueCq3tqFFRq6nz9Or1FtaNsNcwvBH1ifvH\nJAX7ADhkka/hXqik/2ZdRvyjR49i6dKlWLhwIa6++mpceOGF2Lx5M8aNG4fNmzfjoYcewkUXXYRg\nMGjdJxgMpoW3zjQ3+3u/+gJobPSW3ZqIqPD43ifqf/i+p2IItvoAAOGY1qvXm/94q/GnIiKeuL8a\nMIaORIIRvoZ7qBzf952Fx06nQba0tOBrX/sa7r77bsyfPx8AUFdXh5pEGXbw4MHw+Xw4++yzsWPH\nDkSjUfj9fuzduxfjx4/P41MgIiIiIqo81p61XrdBJvaspbRBcsBI/9Hpq+bRRx+Fz+fDunXrsG7d\nOgDAmjVrcMcdd0CWZdhsNtx3331obGzEokWLsHDhQui6jjvuuAMOh6MoT4CIiIiIqFxZgaq3YS2Y\n2LPmSdmzJpthjQNGql2nr5oVK1ZgxYoVWZdv2bIl67IFCxZgwYIF+VsZEREREVGFMwOVdZB1D6mB\nAASbDWJKIYTnrPUfPBSbiIiIiKhAdKWPYS0YSG+BBCCIIiAIAMNa1WNYIyIiIiIqELMN0qyG9ZQW\nCED01GRdLkgSR/f3AwxrRERERESF0oc2SF3ToEUikNzu7CslmW2Q/QDDGhERERFRgViBqjdhLZ44\nUNtuz7pOkCXoqgotGkXs6JE+rZHKF8MaEREREVGB9GXAiBnWRFuOsJZog2x55insv2clVH95nR1G\n+cGwRkRERERUIOa+st6cs6bFYsZ97bas6wRZTrRYGoNG4q0tfVonlSeGNSIiIiKiQsmorGnxGA7c\ndw/aX/trl3fV44mw1lFlTVUg19UDAJS2tnytmMoIwxoRERERUYFk7llT29sRPbAf4T0fdn3fWKIN\nMkdlDZIEXVEh1yfCWjvDWjViWCMiIiIiKpDMPWtmO6Q50r8zWqeVNdmorNWzslbNGNaIiIiIiArE\nCmuJkGaet9adsfu6tWetozZIFVKiDVJtb8/Leqm8MKwRERERERWINWDEnAaZ+LM7B1onp0F2PGBE\nrq8DAChtJ/OxXCozDGtERERERIXSYRtk15U1ravKmqJAdLkh2GxQfL58rZjKCMMaEREREVGBJAeM\nJNogzbDWrcpaJ3vW5ES1TdchOp3QI5E8rJbKTc8PfCAiIiIiom4xB4lYlTVRBATBqrh1et9OpkEO\nuPQyuM48E4IoQrDZrWEkVF0Y1oiIiIiICkRXzAEjknWZeUZaVzqbBumZfDY8k88GAIh2O9RgIB/L\npTLDNkgiIiIiokKx9qwlaySCLFshrjNmZU3Idc5aCsFuh5a4LVUXhjUiIiIiogKxKmhSsrKGxHCQ\nLu+rmNMgsytrqQS7HXosCl3Xe71OKk8Ma0REREREBZJ5KDaQqKz1cRpkKtFmB3S9W/vgqLIwrBER\nERERFUjmodiA0RKZuWctV1UsOQ2yizZIhxHmtFi0T2ul8sOwRkRERERUKKoKCIIxBTJBkKS0Kli8\ntQWf3nkr/Dt3pN3VmgbZRVgzr9e5b63qMKwRERERERWIrippLZCAOWAkWVlTTp6E6vcj8unetNtZ\n0yC7aIMU7A7j9jGO7682DGtERERERAWiK2r6cBEga8CI6HIDALRwOP2+sY5H96cyp0XqPGut6jCs\nEREREREViK6quStrKW2Qojt3WLMPGQq5YSCkxPUdEROVNy3KsFZteCg2EREREVGBGG2Q6R+5jUOx\nk2FNcrkAAFo4lHa7Qf84HwOvmZcV9jKZlTdW1qoPK2tERERERIWiqhDkjMpaYsCIrutQQyEIDgcg\nilBDoay7dxXUgGRlTeeetarDsEZEREREVCC6mr1nzRzjH/rwA+y99WaE3n8PotOV1QbZXeYAEg4Y\nqT4Ma0REREREBdLRnjUAiB78DACghkKQ3O4+hzVW1qoPwxoRERERUYHoSvaeNbPSpra1Gd/W1EB0\nubL2rHWXmNizpnHPWtVhWCMiIiIiKpRclbVEeFPa2wEAkicR1iIR6JrW44dgZa16MawRERERERVI\n7j1rxvf+7dsAAFKNp8Px/d3BASPVi2GNiIiIiKhAcu5ZM9siE1U0qaYGknUwds9bIQWbcSg2B4xU\nH4Y1IiIiIqIC0HUdgihCdDrTLs8a5e9wQkyctZZrfH9XzLCmK0ovV0rliodiExEREREVgCAIGPbN\n2yA3DEy/PPOQbEGA6PEAALRgsOePk5gumXrQNlUHhjUiIiIiogLxnDU5+0JRyLpIqqkBAKjBQI8f\nwwprSrzH96XyxjZIIiIiIqIi0sKRrMussBboRViTzLDGNshqw7BGRERERFREWsq+tEH/9GUAxvh+\noJdhzcawVq0Y1oiIiIiIikgNGfvSXOPGo+GKqwCktkH2Ys+auQeOYa3qMKwRERERERWRWVkzJ0AC\nycqa1pvKWmLPmsawVnUY1oiIiIiIish+yjAAgHPUaOsyMQ8DRlhZqz6cBklEREREVESDv7IIrvHj\nUTvz89ZlotMJSFLv9qzJ3LNWrRjWiIiIiIiKSPJ4UD/r4rTLBEGA5PH0qrIGyThkm2Gt+rANkoiI\niIioDIguN7RwuMf3EwQBgixDVxnWqg3DGhERERFRGRAdDmjRWK/uK8gy9DjDWrVhWCMiIiIiKgOi\nwwE9FoWu6z2+ryDbWFmrQgxrRERERERlQLDbAV2HHo/3/M6yxMpaFWJYIyIiIiIqA6LDAQDQYz1v\nheSeterEsEZEREREVAYEux0AoEWjPb8v96xVJYY1IiIiIqIyYFbWehXWJFbWqhHDGhERERFRGRDt\nZhtkL8KazcZz1qoQwxoRERERURkQ+lRZkxjWqhDDGhERERFRGTDbIEMfftDj8f2CzQZoGnRNK8TS\nqEQY1oiIiIiIyoBZWTvx/FZE9+/r2X0lCQBYXasyDGtERERERGVATEyDBAAhsX+tuwSbDQDDWrVh\nWCMiIiIiKgNiSkCzDWzo0X1ZWatODGtUUPuO+rDu2fcRjvIvDiIiIqLOmG2QACA6XT27r8zKWjWS\nS70Aqm6PPLULvmAMwwa6ce3nTy/1coiIiIjKlujoWetjKkE2PtYzrFUXVtaooCRRAAA0t4VLvBIi\nIiKi8qYr8V7fV5DZBlmNGNaooAbWOgEAJ3w9Py+EiIiIqD+xDWoEALjPmtzj+ybbIHsf+Kj8sA2S\nCqq+xphq1OqLlHglREREROXNPvQUjLr/IcgDejZcBEitrKn5XhaVEMMaFcVJPytrRERERF2xDxna\nq/uxslYQODl9AAAgAElEQVSd2AZJBRVTNACAqunQdb3EqyEiIiKqTuaAEaisrFUThjUqqHgirGV+\nTURERET5w2mQ1YlhjQoqriYDGs9aIyIiIioMQTLDGtsgqwnDGhVUPJ4MayGGNSIiIqKCEGxmWGMb\nZDVhWKOCSq+s8S8PIiIiokJgZa06MaxRQcXiyYDGNkgiIiKiwkhW1vh5q5owrFFBcc8aERERUeEl\nK2v8vFVNGNaooBSFe9aIiIiICo7TIKsSwxoVVExhZY2IiIio0Di6vzoxrFHB6LqedrYawxoRERFR\nYTCsVSeGtSqm6zp27zuBSKw0b1olsV+t1m0DAIQi/MuDiIiIqlf0yBHEjh4pyWMzrFUnhrUq9llT\nAD984l385Z3DJXl8s6o2sM4FAYDxP0kn/VHoul70dREREREVwtGf/wxH1v20JI/NsFadGNaqWCBs\nnLOROuSjmMyw1ljvxLKvTMPcmaOt6w41B3DX2tfw//5Wmt8+EREREeWbIIqIn2gtzWObYU1lWKsm\nDGtVzAxLslya/5vN4SI2ScT40+pR47JZ1x1rDQFA2p42IiIiokomeb3Qo1FosVjRH9sKa3GGtWoi\nl3oBVDjmnjGbVJqwZgYxm03Kus4XMv4S87rtRV0TERERUb41PbYR9qGnQKqpAQCoAT/EhoFFXYMg\nG78UZ2WturCyVsXMA6lLVVmLKx2HRV/QCGvm8BEiIiKiStX+11fh37kDUo0XAKAGAkVfgyAbvxxn\nZa26MKxVMaWTsFQM/rARyNzO7AKuP7GfjpU1IiIiqmS6ogCaBtFmT1bW/P6ir4OVterEsFbFzDZI\nuURhrelEGAAwZIAr6zp/orLm9TCsERERUeXS4sYvoAW7rTwqa5wGWVUY1qqYNWCkZGHNGCIypMGd\ndZ0vZPzFVuPitkkiIiKqXHpimIhgs0PymmGtBJU1iaP7qxHDWhUz96zZSrRn7dhJI6wNzRHW/KEY\nalw2SCJfgkRERFS59LgR1kS7rbRtkLZEGyTDWlXhJ+Uq9FmTH9/44ct495MWAIBNErq4R2EcPxlG\nnccOlyO7euYLxuDlcBEqoY9OfIIVrz2AplBzqZdCREQVTIsl2iDTKmslaIMURUAQGNaqDMNaFXph\n20HE4hr2HvYBKN00yIsmn4I5M0ZlXR6OKghGFAysdRZ/UUQJH538BCejbfBFc//2U9M1bNz9G+w8\nvqvIKyMiokpiVtYEe+qAER+O/vxn8O/YXtS1CDYbw1qV4YahKuS0p59rVqo9a7mCGgA0nex4LxtR\nsZyInAQANDjrc15/JHAM25vexfamdzHt4u8Xc2lERFRB9ERlTbTZIHmMsBY9eBDx402AIMJ77nlF\nW4sgSQxrVYaVtSqUGdZKtWetI8dOdLyXjahYTkTaIEBAvaMOAPB+0x68dPBVBOPG6zOqxkq5PCIi\nqhCaWVmz2SDIMkS3G8qJVuMye3G3fAiyDWBYqyqsrFWhrLBWospaR6yR/g3ZI/2JiuVktA11jlpI\novF+ufflRwAAiqrg0lGzEdMY1oiIqGvWNEi7cRyRVOM1qmoARFtxjygSZJmVtSpTXp/iKS9scs/b\nIGNxtVDLydLcZoS1wQNYWaPSUDUVbdF2qwVS0ZL/sAWUIABW1oiIqHv0eLINEoC1bw1ITmgsFtHl\nAlImbeu6Dl3TiroGyq9OK2vxeBzLly/H4cOHEYvFsGTJEpxzzjlYsWIFfD4fVFXF97//fYwYMQJP\nPvkktmzZAlmWsWTJEsyePbtYz4EyqBlvyq4GjLz+/lFs+u89uO/GC4qyjywaM4KhK6MCSFQsbVEf\nNF3DAIcR1iJq1Louqhhfh+LhkqyNiIgqi5ZVWUsJa/biVtaGfu3raZW1Yxt+Dv9bb2Ds2vUQHY6i\nroXyo9OwtnXrVtTX1+Phhx9GW1sbrr32WkyfPh1XX301rrzySrz55pv49NNP4XK58Pjjj+Ppp59G\nNBrFwoULMXPmTNiL/AIlg6Lqad93Nbr/0PEgFFVHIBLHkEIuLME8/61Ug0+IjgSPAgBO8QwFAESU\nlLCWqKiFlFDxF0ZERBXHmgaZaHmUvLXWdWKRK2vOUaPTvve/9QYAIN7aCsewYUVdC+VHp5+WL7/8\nctx2220AjDKqJEnYuXMnmpqacMMNN+B3v/sdPve5z2HXrl2YOnUq7HY7vF4vRowYgT179hTlCVA2\nRU2vrHU1YCQUNX4D485xHlohKCU+rJvoM98hAMCI2uEAgIgSsa4zq2xhVtaIiKgbrGmQ9hxtkGVS\nuNDCuX8BeXzLb+B7680ir4Z6otNP5x6PBwAQCARw66234vbbb8e3v/1t1NbWYtOmTfjpT3+KX/zi\nFxg1ahS8iUMAzfsFunkYYGOjt+sbFVk5rqkn7I703+IMHVIHUey4umZGu1OH1aOhCGefCaIIQQCG\nDqmFIGSvKxJT8MzLe3HpBSMwsI5DSCj/mvYYG7/PGXUG6p1etCLZkquJChobvdA/S+7jrPS/E4io\nY3x/U19FHcYvn+sH1WFAoxfRwQ04mbiutsFb0tfY3xN/eoQ4BmasQ9c0/P3Pf0LthDMxZs6Xir+4\nEqqk932XpZSjR49i6dKlWLhwIa6++mo89NBDuPjiiwEAF198Mf7t3/4NZ511FoLBoHWfYDCYFt46\n09yc+0DaUmls9JbdmnrK54+kfd/a2nlwbvMZt29vC+Gtvx3GOeMGFbRFMRSOQZZEtLTkXtcH+0/g\nNy/sQTwaxxXTRxZsHdR/fXbyCGpsHsT9Apr9fhxtOWFdFwiH0dzsR6u/3bqs0v9OIKLcquHffCo9\n/0njNeQLq1Ca/Qhryc9QwahWstdY+OOPra9PHjoO7fTsdYhOJyK+QL96H5Tj+76z8NjpJ/KWlhZ8\n7Wtfw91334358+cDAM4991y88sorAIC3334bY8eOxdlnn40dO3YgGo3C7/dj7969GD9+fB6fAvWE\nqmZP/WlpCyOu5J4GFIoqkEQB7+9rxbpn38e7H7cUdH1xRe80DNoT0yzN9kyifPPHgqhzJPcUpA4Y\nMb8OKUYbpCzyhBMiIjLEmpqgq+kTtM09a+b+NNGZ7AoSijy636SGgjj4vfut7xW/L+ftRJerwxZJ\nKg+dfgp59NFH4fP5sG7dOqxbtw4A8NBDD2HFihXYsmULampq8MMf/hB1dXVYtGgRFi5cCF3Xcccd\nd8DBiTMlkzlgxB+KYdn6N3Dl9JH4py+Mybp9OKrA5ZDhSISklvZI1m3yuz6t0/1q5jlxYYY1KoC4\npiCiRuC1JfcUpO5Zi6rp0yAFdD6gh4iI+ofwx3/Hwe89gNoZMzH0a4uty7XEnjVzf5roTG4pKfah\n2NaaounHz6gdhjU3lJMncl5H5aHTsLZixQqsWLEi6/KNGzdmXbZgwQIsWLAgfyujXlMyRvcrqg5d\nB1o7CGGhqAK3Q0a91wjYbYFoztvlbX2q1umESqfDDGvFO/uN+o9AzGi/rbF7rMvSRvcnvj4ZaQOQ\nfgYbERH1X+G9nwAAfK+/lhbWrGmQcqKy5kpW1op9KLa1JiWe9r3qz932J7pc0I6EoWsaBJGD38oR\n/1+pQkpGu6NZqXrzgyasfea9rNublbU6j/EXSqHDWlzVcrZBHmoOYPWmt9HmN/7Si8T4IZnyzx83\nwlp6ZS2a9rWiKfDFjH/YdOhQNf7igIiov+sozOhZlbWUNsgSTYPU4+mfoVRf7rAmuVyArkOLFvaz\nH/Uew1oVymyDdKQcPr3jo+a0Q7MVVUMsrsHlkFDrsUMA0B5IL53nfX2KlvOg7r8fbMOBY340txvt\nZ5EYPyBTfgTiQTz2wRM4HmpBIGYMQ6qxp4Q11ag6e2010KHjeKgFOpLvI0Xna5GIqN/LMcEaALSI\n8bnFbH8UXSltkEU+Z82UWVnzTJmS83ai2w2g49H+VHrcOV+FzDbIUwa6MWqoF6IgwGmXrPATjWlw\nO42wZF7mcsiQJRFet60olTVbjsqauUetxmWD3SZyzxrlzXstH+KtYzswtv50SILx2vPaUtogE5W1\nOkct/PEAjgab0u6vaAocUnmclUNERCXSQWVNDYUAUUyGtZTKmliqypqS/Aw1YsWqrMOyTaLLDGs8\nW7RcMaxVIbMN8r4bL7DOV3M55GRYi6twO43/6zMPxK6rceDg8QBO+CIFO3NNUfSclTVzj5rLLsNp\nlxFmZY36SNM1/P3kXhwPNQMAGl0NOOA3DsROray5ZCcckh2NroE4FDiSFdbiWvpvKImIqP/pqA1S\nCwUhut3W2bFpA0ZKVVmLG/9uNcyZ22FQA5L767QQw1q5YhtkFVI0HYKAtIOwHbZkK2QsngxBkURY\ncybCWmO98aZ9/IWPCrI2VdOg6XruylrMXIsEl12y1kbUW3/Y9yf85N1f4H8O/AUAMNjdCE03fpnR\n4Bxg3e7aMVfip3PuQ6N7EIBkOBvobAAAKNyzRkREHVXWgiFI7mS3Ruo+tVKN7jcra4Kcuy6jtLdD\nV1VjzxoAlW2QZYuVtSqkdtBmaIomwpqu69bZa/ZEpesrXxqPnX9vRrAXQUnXdeu3Sh1RFGMfUK4B\nI+GUKp/TIaOtwHvnqPrtbk3+0sEh2VFr9+Ifhl+IEd5TcZp3mHWdJEqoc3rxpRGzMKFhPEbVjsCZ\nDePwzvH38NqRtzgRkoiIIAgdV9bkAQNSbpf8LCSWaHS/WVnLVdlTw2Hs+/a3UH/xJbA1DgbAPWvl\njJW1KhRXdEgZYUjTksMSYnENx0+GcNuP/4q39xwHkAxPA7wO2G1ihwdodyQaV3HTwy9jy58/7vA2\nmq7j2z9/I/F42aEukmiDdNpluOwSonE1bd1EPSWl/MM62DUIgiDAKTtxZsO4nLd321wYP2AM7JIN\nExrGw5Y4EJthjYiIIGZ/dtHiMejxOKTEoI5M5VhZU9vbocfj0MLh5J41tkGWLYa1KqRqWlYY0vRk\n6Hng1zvw3qcnEAjH8elR45DE1EOqbZKYNf6/K8dPhqFqOv7n7YOd3s6cNJnrUOxQNNkG6bQbf7lw\nfD/1hSgk23+H1ZzS4/vLibAWZ1gjIur3dDX7s5EWNCpSYkobZCpBknJeXmhWZU3OrqxpUWMCsmB3\nQPJ6AXR8aDaVHtsgq5CS4xwzXU+vUB1uNs6aMqtZqbeXZRHxHH8hdSbz5+ciprQF5GrTjEQVOO0S\nREGAK+VgbLezNC0EVPliKYddn+Yd3uP7y6ysERGRSc3+t0ANGcfBSJ70ytrAa+YhduxYUZaVizm6\nX8xog4w1H0fT478yrnM6INfVAwCU9rbiLpC6jWGtCimqnqOyln6b+hoHgGTlKrOy1tM2yFgPb59r\nGmQocTg3AFbWKC/aY8lDQHsV1oREWNP5OiQi6u90NXvYVEeVtYFXX1OUNXWkozbIgw/dD7W9HQAg\nOpyQ6xNhrY1hrVyxDbIK5aqsZe79qqsxeqjNcf6p4c4mi1B6WFnrbqgyp1LKKROV9h31IRxVEImp\nVliz24zrexoCiUyqpsIfC1jfn9qLNkibxMoaEREZcoU1q7LWQRtkqejxRFizpYc1M6gBgOBwGEcO\nyDKUlMupvDCsVSGjspb+f+1ZoxvSvq/Lc2Ut2s0z0Rx2I6yZB3ef9Edx36+24/nX9yMcVaz2R7ts\n/Jl6zABRT5jj989pnIwHZq6EU+75uYFWZY2j+4mISMuxZy1sVtZcWdeVktkGmWvPmkl0OCAIAqT6\neqhsgyxbbIOsQkZlLb0N8v9cfgbcThv+tN0YAFKfqKwpavYofZvc87AW6WZYc9ok+JA8PsCs4DW3\nR6BqOlz29MpaNM7KGvWOU3bim+d8HUM9g1Hn8PbqZ9isASM8FJuIqL/LVVkzL+voPLNS0ToZ3W8S\nHcYvMeW6ekT2fQpd0zo8+JtKh/+PVCFF1bJG99tkCeed2Wh9X5MxtCN14IcsiVA1PW2CZFdSw1pn\nw0bsiTZIsxJntkW2B4xBEObh3OblrKxRX5zZMA71jrpe39+sxoUUjjQmIurP2l5+Ca3PPZN9ReKX\nzuUWcnLtWTMDnEl0GF1Wcn09oGlQ/X5Q+SmvV1aFO9wcwL//19/gD5XuMGdN06HruactmgEIyB7w\nkfq92RLZk/H9qXvWOttn5ky0QUaywprx38xttkGaYU1hWKPS8diM6V7BOA8LJSLqzyL796d9b/5i\nWtcTn3nE0ozo70gyrCV/Oa+cOJF2GyER1qyz1iKRIq2OeoJhLY/u2fg2du1txWvvdT2qNa5o+O3/\n24v2YH6DnSAAk0YNwKSMPWpARljLrLxJOcJaD4aMpFbWOmuJNPesmW2QtkS7Y1swUVnLaIOMsQ2S\nSshjMzaMM6wREfVvWe2EZkuk2QZZdpU1sw0yWVlTg4G024hOZ+I2trT7mNpeehHhvZ8UcpnUDeX1\nyqpgmq5DTUxc9Lq7Phfsd6/vw/OvH8DPnnkvr+sQBAF3XTcVV04fmXVd6hARSUzf05ZaWTODXE/2\nraUOGIl2Mhnyi+eeavx5nvGnKAiwy6IVytxmGyQHjFAZqLEqa8ESr4SIiEop87wyM9jo5rTtcgtr\n1qHYybCmx9ILBKI9UVkzw1rK9Yrfh+O/+TUOPrim0EulLpTXbsgKlhpWunOgdGu7UUlq9RWv5GyO\nxR8ywNWtylpPDsbubmVtythBWP+tL8AmJ6t8dptktU46rdH9iQocR/dTCbENkoiIgOzKmh5XACcA\nrUwra9boflvKZRl71pxGWBPsxm1S97RpIf67Vy4Y1vLE5ZAxf9YYPPXy3m617qmJ8a+Zg0AKyeWQ\n8fCSGXA7ZUgZ0yJTp0eaQS4UUYBuzmZI3bPW1WTI1KAGGO2ZgXA8sUZzz5rZBsnKGpWOXbLDJsoM\na0RE/VxmWNPicUgAdHOcfxE/z3VHrtH9WkZlTUhU1szbpIY5NZj8d0/XdQhC+udGKp7yemVVuBGD\nawAA8W4MxVATI/Mz2xELbWCdEy6HDFEQ0h7blmPAyD0b3+72YdeRePcqa7mY+9gAJEf3W22QrKxR\naXlsHoY1IqJ+LquylhjgYZ69VnaVtRzTIPV4RhtkYsCItWctrbKWbP/nlMjSKq9XVoUzQ87Tr3yK\n/9n2Wae3Nfe3SSV8c0s5qmlAekukeQ5bpvZgDN/95Vv4YL8xWSia1gbZvYBnctiSj+dyZp6zxsoa\nlZZbdjGsERH1c9ltkOaetTKfBmlL3bOW3gYpSMaaxRwDRtSUsKacaC3YOqlrDGt5ZE+Ztrjlpc6n\n5yiJN7fZfqhqGh574SPs3n+is7vllZwSFNMGjKR87Xbm7pR9a/cxHGoO4gdb3gUAXDBxiDVYpceV\nNVt2Zc3B0f1UJjw2NyJqBKrG1yIRUX8lyh2EtXKdBhnP0QaZqKy5xo3HgMsuty63KmspYU5LaYOM\nfvYZjjy6FtGDBwu6ZsqtvF5ZFc4ud/8/p9UGmQhrew/78PI7h/HDRPgphtR9arkGjADGtMZcaj32\ntO8vnnYqbrjiTACJvW49kBpyXZnnrLENsl85GmzC/xz4CzS9fP5/n9hwBkZ4T2W/PhFRP5a1Zy2c\nCDNWZa28PlI7Tx8D15kT0v7tMsPYgCuuROOXr7MuF+zGZzpzwIji96Hl6Set69VwCIHtb8O/c3sx\nlk4ZOGAkj1JDR1fM6pP5Hm8LRAuxpE5JHQS0XAdqxxUV7+87gXPGDoIgCGmTIuOKCpssWWP3Q9G+\nhDWzssYBI/3R9qZ38cf9f8YI76k4s2FcqZcDALh01GxcOmp2qZdBREQllBnWlLY2AMk2yHKrrDXO\nX5B1mblnTbSl/8I9c8BI829+bR2QfdryldZz46HZpVFer6wKl1lZM0+3zyUUMd4QZhg5diJZbjYn\nRRZaamWtq0EnW176BD95+j28uOMQgPSKVyBshDMzaIV7GNbS9qwl2iBlSYQAhrX+Zmz9aADA+60f\nlnglRERESVlhrd0Ia+bo/nLbs5aLOQ3SrKSZMgeMaNFkAUFyeyA6XcblkXAxlkkZGNbyKLOy1lkL\nXzDRKmgO0GhKCWut7cX5zYU5VESWxLQyea4BIR8fNP5S+mBfYqBIYt3XXjQaA7zGNCF3r8Na6plr\nxpoEQYDdJvGctX5mbP3pcEh2fNj6d+syXde5X4yIiEoqO6y1A0geil1ulbVcrHH+Gc8lc8CIfdjw\n5HUeN0SXEwCghVlZK4Xyf2VVEFtGZc08OyyTqmlWoInEVGi6jv3HkmNRU6tshWSGtcx1h6PGB+PU\nEFXjMt7IgYyK4PjT6q3bmJMcexrWzFArS0JaaLTbRFbW+hmbKGOYZyiOh1usgPbbT57HvW8+zMBG\nREQlI2YEHNWsrJkDRsrsnLVczD1rYgeVNavyljLuX3K5WVkrsfJ/ZVUQWeo6rB1qDmDx91+2RvdH\n4yp2fNSMo60hq6r0WVOg24/5+Asf4YmXPu7leo1gZMs4INucVGkGNABwJtoTA6G4tW4g9xlpPQ1r\nLe3Gm3/SqIa0y+2yxLDWDw12N0LTNbRGjCruyUgbWiInEFQ4Pp+IiEpDSN3nJQjJPWt6eQ4YycWc\nBilk7llLhLfMCZfeCy6EIMvG9YIALcywVgrl/8qqYGYVKpWmpe9jiysaapwyJo4agLv/11QAwIFj\n3T98cNfeFuz4qLlX6zMHjMgZlbVrZo7GpFEDcMeCKdZlZmuk3wprxl9Oqa2foijAYZd6PA3yny8e\nh7NGN1jTJE12m2g9DvUfg92DAADHQy0AAI/dAwAIxIId3qeYDgeO4jd7nkJc69nrnIiIKldq66BU\n403uWVPLc8BILrq1Zy29Spg5YMTchzfgi18yrhcEiC4XB4yUCKdB9pGm6fjT9oM474zBGFjnTLvO\nrEKlGjHEiy+eeyp27z+BAV4HPth/EqNOqcW3rpsKXdfhddvSWiK7YrdJCHbQbtkVOTFUxJGx167W\nY8dd1xnBsbU9gh0fHbdC2uABRincPATbkRH03A65x9MgRw714s5/PifrcrtNQkzhXwz9zWB3IwDg\neNgIa15bIqzFg/hb8244JQfOaBhbsvU9sO3fABgj/c8ZPLlk6yAiouJJDWvuiZOs0f3leih2LmYY\ny2zpFDMGjJiVNUgpv5B3utgGWSIMa32090g7nnjpE8TiKq6eOTrtutQWwVQLvzQeAPDoc+8DMPat\nuRwyBEHAhJEDsOezNui63q1znew2CSd8URxvCyMWV3FqY023124Oq/S4bB3e5tVdR7D1tf0AgMZ6\nJ1Zefx6A5J41e8ZzdDlktOfpGAKHLCIW1/D+vlZMGtXAc676iRHe4RAF0Zqm6kmENV/Uh1/veQoj\na08tWVhL3TcnVcA/zERElB+pYe2Uxf+SnPitleeh2LlYe9Iy2yCtsGZcr5vVwtSw5nJBOXmyGMuk\nDAxrfdQeMF7YTkf6f8p//d/TMO7U+lx3sZgHS4spY/NvvGoConGt28HECDQqfvCf76ClPYLvf+NC\nDKp3deu+wUSbZo2z47CWehZbjctmrSuqZA8hAYxDrY+1qt0Om50xg+CPnvgb7vnq+RgxxNunn0eV\nYZBrINbM+A68ifbHmkRY+7h9H+JaHEMSlbdS2O87aH0dVWMlWwcRERVXZjXK/IxjVdYqYcBIPA5I\nUloIA1IGjGS0QaYGUNHphBYJ5+XzHfVM+b+yypwvZHxgq3Wn/5ZiSIO7y/vOnTka3/7KNNR5kve1\nyZI12KOzc9pMdpsEHUBLYtz/SzsPd3fp1gCUGnfHYS01jKVW4GIxFQKyz5ZzO2zQdN0aQNIXDpmV\ni/6qzuGFKBivrZpEaDPH+Z/mPTXttrneJ9157/TGZ/5D1tcxhjUion4jc9y9pUwPxc5Fj8WyQieQ\n3MOW3QaZLESILhegada+Nyqe8n9llTlzL1dtRuBxddACmarGZUsbfZ/p+795Bxv/0PnhwJlnu110\n9ildPq7JCmudtEE67dnj+wFjwIjdJmX9dsXjMt7YwXDfhy/YUw7LdnbjvydVJ7OyZk6HHOFNnv+i\nairufeth/H7fn6zLHt21ET959xfQ9PwPpznoT/4yhJU1IqL+I3WcfapK2rOmxWNZLZBAjgEjVhtk\nemUN4Pj+UmAbZB+ZlTVvorK2dN5ZOHg8AFseqkKHmgM5J0qmSg00k0Y3YNggT7d/vqIa1YfOwlpa\nZc2RGtZUOGzZWd9sqQyE41kDV3oqNYhmtplS/2GGNVODcwCiagwbd2/G5EETcTzUgqOBYwCMAPVe\ni/ELju1N7+JzQ6fldS3pYS0/ezOJiKj8ZbYOWipqGmQ8axIkYLR0CrJsHYqtW2fHpQ8YARIHY9cV\nYbFk4SfgPvj0iA9/SbQdehOtjOeeMRjnnjE4Lz9flkUoSufVAbucu/LVE52GtdRz1JzJr6NxNauq\nl/qzugqZ3ZH63Mwz3Kj/8WSENbfswmtHtuG9lg+tYGaTjNfdIf8R63ZOyZH3tYypHw2X7MTe9v2s\nrBERVRgtFkPLU0+gbtYlcAwblpefWUnnrNWcM7XDdk7BZoOWODRbN4dppVQLJa8xN0D1+4ChQwu7\nUEpT/q+sMvbDJ96xvq5x5Q4TJ/1RvLH7GHyhGF5+5zAUtfutWTZJRLyL26dW1jobFJLLl2eNAQCc\nPWZgh7dJray5UyprsbiaNVwESO5r6+1xAqlSn5tN5ku1v7JLNtgl45chDskOSZTwafuBtNvYROP9\nZ1a+rp94Hc5unJT3tVx3xjwsGH8tgOK2QWq6htePbENYYfsJEVFvRT87gLaX/gz/tjd6dX+xpgaO\nUemTv6FWzjTIwQv/Nxq//M85rxNstqw9a6nPydZgfFaMt7YUeJWUieWKPghHU8d4536T/nzrbnx0\nsA2nNnpwqDmI4Y2eLqdEmmyyaO0r60j6AJCe/d95xfSRuGL6yE5vk7pXzOUwvj5wzI9QRMk5ndGq\nrOUlrJV//zcVR43NgxNqDG7ZGNzTHm1Pu94m2nAseBzvtXwAIH1fW745EhW7YrZBvn5kG/7zo99i\nR7H5LHQAACAASURBVNPfcMvUxUV7XCKiqpKoFOmx3n1GGfOjHwMZe/UraRpkZwS7PWXPWiKspXQ4\nyQMbAADKiRNFX1t/V9mvrBI7ZWDXEx+PnjAOTTzUHATQswqRLIkdVuIiMQWKqqUFmt62QXYmNQy6\nEvvGXnvvKHQAV12YHfTyGdYyD9ym/qvGZrzX3DajZ/7rkxdhmCfZhmETbfjJu7/AnpMfwy7ZrYO1\nC8EhG1W+Yk6DbI0YZ9t8dPKToj0mEVG1sQ5/Vnr3GUUQxeyx9eY0SKGyP7OIcnZlLbUN0jZwEAAg\n3tpa9LX1d6ys9UEoYkw8vH/xBR3eZsTgGry/L/lbCHcPWhVtsoh4jj1ruq5jxYa3MP7Ueowamqxu\nFSKspVbW3ImwdsX0kZg6vhETRg7Iuj0ra1QIZkVNEozXhMfmxmne4TgSNAaL2CQbGpwD0BZth9fm\nscb+F4JdNMJaMdsg5cTz1lGYIwmIiPoDwWZ8jtGVvk+sNiWnQVZ2WBNstmSI1bIPxZYTbZDKCYa1\nYqvsV1YJaboOfyiOscPrcMrAjicwxjLClsfZ/Xxsk0Qoqp51ZlQkpuKEL4pwVCl4Zc2eo7I2wOvI\nGdSA1NH9fQ9r3KdGJslsXUkZx9/gTLYT20UbRibOX8scSJJv9sQwk2K2QXKYCRFR35nj9/V4/sIa\nNA0QhIrYs9YZY8CI8W9NsrKWfE6SywXR5ULwvV3Ye8ctiLMdsmhYWeulUESBpuvwdnKgtHm7VK4e\njKCXE2FFUbW0owBSjwtIHcIxoLZvo/JzSd+z1vXazcDoz0NYIzKJibYTLaWyVGOvsb62iTZcMfqL\n8McDuGzkxQVeiwi7ZEdMjeGNI2/j3eb3AADzx12DRnfHw3r6wh8PWF/ruo5XDr2O9pgP14y5oiCP\nR0RUjazzxHrZBpmLrmkVX1UDEod+qyp0TTPCmpR9lm7DlVcjuOtdiDU1xiHZVBQMa73kzzhfrSPh\naHpYEzN7nTthS2xWjSvJsPbi9oPQEp9XvR5b2nj7wfX5f+PIKRtm3d2oCjpsEiafPhBjT+37IRyq\nxpYvMoiJNsDUg65dUvKXEzZJhsfmxlcnLSzKehySPXGm2wd4v3UPAKP6dfu0bxTk8fyxZFhri7bj\nvz5+DgAw9/TLs/dPEBFRTmZlTYvnMaypasVX1QBYh2Xr8XiHz6nhiivRcMWVxV5av8ew1ku+oBHW\naj2dV9Yyw1pPmJW1eOLw6rZAFL958WPr+lq3Pa1NsdBtg92prAmCgDsWTMnL42kMa5QgIlFZS2kJ\ndsjJc9RsYv5bgDvjEI2wVp/SihnX8thWkyGQCGsOyY59vs+syxVNsc6YIyKizgnWgJE8t0GKlb/H\n3hq+Eo8DmtbxIeBUdJX/q4Ai8odiOOGLAID12+zB9R1PhNR0HeFY7/9CSFbWjN7hzAOyvW5bUQON\nvch7yE4bYrS5TR03qKiPS+VnSuNZAIDPDZ1qXZZ66LW9yGGt3lkHmyin7Zszz3orBH88iIHOAfje\nRasQjIesyyNF3DdHRFTpChHWdE2DIFZ+h4P530ZLVNaqIYBWC1bWeuDeTdvR6otg/bdmYdypdVj9\ntc9h2KCOw1o0pkLvQ5ayycabX0lU1jIPyE6trA0bVNihCgCK3m41Zlgd7rvxcxg8oOsjEqi6nT90\nKk71DsOQlJH8Ljm1DbK4Ye3rZy2CoilpVS5zUmUhBOJBDPMMhU2yYcYp5+P1I9vwmf8QwkoE3pS9\ne0RE1DFBFAFRTJ4nlg+asb+r0gmplTVV7VZlzZyEWQ1toOWMYa0HWhNVtb8fasOkUQ04bXDnH5L6\n0gIJALbEG8Uc3x+LZ1bW7Bg51Itb/nEyxp3WvYO2e2PVDecjGle7vmEBDG/kB1EynOIZkvZ9amWt\n2G2QZkBqj/msy8JKpCCPpWoqFE2xnq8kShhTPwqf+Q8VdSIlEVE1EGQ5r2FN17SKP2MNSA9ruqZ1\n65DvI2t/jODf3sW4RzdY+wEp/yr/1VUCuz/t3rjSUCKs9WRcfypbyjRIAFlnrpmTKKeObyzI2H7T\nyKFejC9gGCTqDWdqZa3IYc00wJF8X6RObASMkPWD7Wvx8sHXcDLShvvf+hE+advX48eIa8aHCntK\n9dCZGK4SKVBAJCKqVsZ5Ynncs6Z2L9iUu9Q9a7qqdKuyFvzbuwAANRgs6Nr6u8p/dRWRGbrGDK/t\n1u3NytrMyafgwklDsGzh1C7ukU5OtEFalTUlvbpV73Vk3ac/eO/TVvzy+Q/Shk1Q/5Ma1uwlGrJR\na/di1qkzAaRPbAQAX8yPfb4D2Oc7gP/e/yKOBI/hP97/dY8fI6qaYS05edaZGK7CPWtERD0jyPkN\na7quQaiC/V1C5oCRHjwnLcZ/iwqJYa0HNF3HaYNrcO4Zg7t1+4G1Tgyqc2LiqAFYfPUknDEi90HS\nHUkd3Q+kH7D9f//X1B4dA1BNXn//GF57/xjaAzwouD9LHSoiF3C4R2cEQcCXx1+DiQ1nIK7F0w6v\nNtsiXbILMdVsuen5ezauGT/TLqaEtURLZKFaL4mIqpVgk/N6zhpUrSr2bCUHjMSgK13vWTP3qwGA\nHuG/RYVU+a+uIorFtbRDqLvSUOvE95fMwNljejfN0Bzd/+4nLQCSoe0rXxqPM0f2LPhVk1hi/1yh\njyqg8pY68KbY0yAzeWzGgJ9ALNkKkgxrTqsC1puJkTE1RxukbLZBGj+3PerH7sR5b0SVRg2F0PbK\nywh/8nHXNybqI2PPWn6nQVZDG2T6nrWuh6aowWQ3icawVlCV/+oqEkXVoGp62iHUhWZW1v684xD+\nfrCNISXBnIrZ3/87UFKp9qyZXIm2xNSBH2ElbFwnOdEcbgXQu7PYYonKmi1tz1r647342ctY97f/\nwPFQcy9WT1RagR1v4/jjm3Bs44ZSL4X6AaMNMr/TIKttwEh3pkGqbe3W11qUYa2QOLqlm8yqVjHP\nGksNI+3B2P9n780DJKvLc//n7LX3Pt2zDzPMwMCs7Isbi0q4yZVExWQSjCIuqIBI1AiIRmMSNPGa\nHwl65ZpF0CBCDBpMUOOGiOzMDMsww+x77921n/X3xznfc05VV1Wf6q7q2t7PP11dder0t6prOc95\n3vd53TV0ukjRNHoeiEIaPRjadboM7wuLhX+EpRCm83Zq5LSahG7qVZVtus6avwzS+XsZRxD2hmyn\n/cD0YSzyjTcgiFYgfsGFEBJdkAcHZ9+YIOYJL0k1T4Nsh+h+XrK/Y9w0yFlKO/WpSd/lKZia6u4j\nCFbA8QAEOWuBYf1ikrRwLyzRZ6uLPOeuYaGHUzcbmmFCFLiO7dkjZlLPgdRB8HrIfM6aI9zCQgjv\nPuNdWBIdggULk/npkvsoh+r0wcklnLUfH/w5Xpvcj+XxpQCAw8mjc38QBNEgeElGbPMWyEOLG70U\nogPgRBGWrsOqVUhZmw3Ftpyh2LM6a8mke/nkP38T+265GWY+WNCIqanY98lbMPrw9+e+4A6is4/6\nq4CVIDbKWcupBjSdlUF29pkITTc7/jkgbD606T24as2V4BtcguL1kHnOGutZC4khbOw/A6f3rgUA\npLXyEccvje3Cl56+Cxkt616nmqXSIL0kzF8ffRLLYkvAgcOh5JEaPBqCaB7SL+7Awb/8C4oGJ2oG\nJzonvozazI+1XajWPybxB4wEKYOMrF8PocsbX8OHQ+DkYM6akUzCmJqENjI89wV3ECTWAuK6Wgvo\nrJmmd9Ynq+ruUOxOd9ZU3aQSSAIAsLH/DLx55ZsavYwZPWRAYRokAEScn34hVsz+qYM4mDyMI6lj\n7nVaiTLI3lA3NvafAQDYM7kXiiAjIceqdu0IotnJvPwy8gf2Qxs+2eilEG0CJ9mVGGatSiENo63S\nIC3HHZtNrIndPRh67/vc3wff/d6C4K9KWKpdMcLLnTmCqlpa/9W1QDTCWZvOeB8k2bzu9axVkUjZ\njui60fGClWgu3LlnJZy1sOOChSVHrOkZd5uD04fx0J4fusOyWapkWvO2YQEj/jJInuPxoU3vwZtX\nvAkRMQyO4xAWwzQkm2g7WBAEJ1KLPVEbXFFSo5ARy7Jm7e9qBVxnjSU7BnhMfMir8hC7ugL/LdMR\na0GduE6n9V9dC4QbMLKAQum05Z69bJdBMmet9e32+UDOGtFshAT7CytbKg3SEWuus6Z7ztoP9z2K\nnx1+DN/Y+a/IaFlEpQiAwlJJNrut1HiCq069Ered/3H372T1XO36MAiiCWDDi0msEbWCvZZqFt/f\nJs6a1NcHAODD9vdQkPAPXvGJte7gI6WYe8cr5KwFofVfXQuE2oB+sZVDcXzyj7YCAHJ5w11Dp7tK\nGok1osko5azlfD1rQOkySCbK0loGuyb2+MSa56y5ZZBC5TOQITEEwzLmNB6AIJoVT6w1NvGVaB/Y\na4m9tuaDOxi6DVIN5cEhrP67r6Lr9W+wrwgi1nzOGh+NBv5b5KxVBx3xBoT1iykLLBL6u50z9qpO\n0f0OJNaIZoMJMn/PWkbPgQMHxRFZEUeIZYtKJeNSDB/dch029Z9RUqx5ASOVD1aZg5fVy/fEEdVB\nLmXjccWaRM4aURtcZ60WZZCOWGuHOWsAIHZ1w7Kcx1SlWAvarwYAluo4ayTWAtEer64FwHXWFjBg\nBABCsv2hks3rDQk5aTYMc+GHkxPEbLAyyJwvuj+tpRGVIm5SpVcG6QmxrJ5DRApjfe86iLyIqFii\nZ80tg6z8peaJNepbqwXf2fUQPvrzT5FT2WBYqRo5a0StKBj+PE88Z62NDqedlEwuQMIlF5pbGaPn\nrFEZZBDoVFVAGpXEGJLtN0tONSAI9lmLTnaVdN0+093JzwHRfLAyyKxvKHZSTaFLSbi/R6SZZZA5\nPYe+cK/7u+us6V7PmjsUexZnLURiraY8fuxJAMB0Pom+cPBeDKK2UMAIUWtqKdZgMmHTPsckllGF\nsybJ6H/H1ZAXL6nub+RZGiQ5a0GgT7+AaA1ytUSBhyzyyOZ1KM7f7mSh4vUOdu5zQDQfMi+BA4ec\nnodpmfiPvT9CRs9iWcz7AgsXBYxohgbdMhAWQr5tQuA5vkwaZOUvNebcUSIk0U5QwAhRa5hAqImz\n5hyTtEPPGsNi8+cCuoW9V1xZ9d+gnrXqoE+/gDQiup8RUkTkVAMrh+LI5HXwVdQFtxvUt0c0IxzH\nYVViOQbCfdg1vgf/c+hXAICY7DVcS7wIiZdcscZcOBbpz/YTEcMFYo2JL0WoXC7CnLUM9awRbYSl\n6wDHtdXBMNFY3Ih6RzDMB3YygW+jkwlCPA4hFoeybHnd/obbs6aQWAtC+7y66kwjRcJAVwiaYeLd\nbz0Nnd7v7v4f2qk+nGgLPn72h8GBw3PD293r4nKsYJueUBdOpE9iLDsB3QkO8TtrANClJDCcGYVm\naJAECUktDZET3MHb5Qi7fXPkrNWWDv/QbTCmpoETxarCCwiiEl4ZZC3EGivTbZ+eSiEcxuqv/H1d\nSztNR6xRz1ow6Ig3ILrTRCo2QCR87OrN+LM/3AqO48Dznf2FVTxrTtO9+XME0Uh4jgfHcZjMT7vX\nxaTCKOMrVl4GzdTx3PB2z1kTC8Xa6T1roZka9kzuAwCk1BRicmzWg1U3YMQgsVZLLBJrDcXSdbcE\n0kgmG7waoh3gJacMUg1WBmkZBoxMpvRtbZpWWu8ePOpZqw4SawExDPsLW2iAWIqGJMTC7XPWZj6o\nRQ7nzXc9jpvv+nUjl0QQBUzkJt3LHAo/L84bOgsf3nwtLlpynhsEUizWNvSfDgB4eexVAHZQSbFD\nVwpKg6wPhkUngxqJpdvOWur557D35huQ2f1qo5dEtDic7JRBBnTWTt77rzhw26e85EcfXlppe4m1\nekM9a9VBr66AGKYj1oTOdrYajeYLGDFNC5k8xWoTzcV4bsL3W+HnBcdxOLPvdGimjn944f8B8IJH\nGKsSK8FzPA5MH0beUKGa2gyHrhQibx+A6BQ1X1NMEmsNxXbWJPAROyk1+fSTiKw7rcGrIloJyzRx\n+Et/jfCaUzHwzneBc521ymLt+D3/F+qxo+BkGUY6bfdOFu+7DcsgFwL23PMKlUEGgZy1gOhOlKnY\nRvGsrYhmeM7a+DQ5CETzMZ63nbVzB7fikuUXl9xG8A1Q5YuGqcqChMXRQRxJHcOUU1IZxFkTnZk4\nhhMlTdQGEmuNhZVBhk9dCz4SQXr7dhpWTlSFmc8j99oeTDz6XwCCp0Emn3wC+cOHYGYy4CORkqXo\nlFZaSHbPHhz9+6/AzFUOuvJ61shZCwIpj4CQs9Yc/OfjBwDYqZwnJkrXkBNEI1ndtRIXLT4X7znz\nj9yExmJ4jsdNWz+AxdFBnN576ozbl8eXQjM17J3cDwCIS7OLNYFzxJpFYq2W0PPZWCxdByeJ4AQB\n4XWnQR8fg5Gi3jUiOMz9AmyXzU2DDBjdr09NQYiUrm7wetbIWQOA9MsvIr1zB3IHDsy4TT1xHGOP\n/BCWZcF0e9bIWQsCnQoISCN71gibZEbF7iNTAIChvghOjntnbkzL6uiRBkTzcPW6qwJtt67nVNx+\n/i0lbzuj9zQ8c+J5pDR7OLZ/BEA5BJ7EWj0gZ62xWJrulpjJg0NIA9BOnIQYT1S+I0E4+B00fWLC\ndXNmK4NkmJk0pEWLSt/m7DvIAOlOQHDKlY10esZtB7/wOVj5POShITe6n0RuMMhZC4jhNJYKVAbZ\nMJg4e93Gxdi0pt+dfQcAhkEHVET7cPbgZvztG7+AxdFBAMBAuH/W+zBnTacyyJpCYm3hsSwL2T27\nkT98yA0YAWyxBgDqyRONXB7RYvjFmnryhJcGWUV0vxAtc8LMYGWQJDoAgHccSDMzU6xZeVugGdNJ\n8IoCoaur7qmT7QI5awHRHWdNpDLIhnHSKXtcs9Q+o/rmc5fjFy8cxchkDppuoc2Sc4kOR+JFnNl3\nOm4972ZXtFWC9cGRs1ZbjBIJcER9ye3fj8N3/pUd6GBZrliThkisEdXjF2vG1BSkvj4AgBkwuh/w\nHKOZ+27P6P65wkRtuVEHgP3/GHrfB2DmKXcgKCRpA+L1rNFT1ihOjNtv/qFe+0NTFHisHIwD8AJg\nCKKd4DgOS2OLZ4SQlELknVlU5KzVFHLWFp7QihXglBDgBImwUil50D5poZFYI6qAiTUhkUBs61le\nGmQVzho/W88aBYwAgJvaapYog2RYugYhFoPUN3vFCGFDyiMgrMyOetYax/CEXQa5qMc7wyU689ZI\nrBGdjues0XuhlpBYW3g4UUT3my7xfnf6gYR4AuB56FNTjVoa0YKwvrKu178RfCjkpUFW46yVKYM0\nKbq/APY8jf/oPzH9xOMlt2FJkERwSKwFxHXWSKw1jM2n9uENm5egO+ZFvbJRChqJNaLDERxnTbdo\nzlotobLSxuCP9GbOGsfzEOJxGEk7DTKz+1UcvvOvMP5fjzRkjURrwJw193UkVTcUG/Acoxn71qkM\n0o/fgTzxzXsKbmPuo5GmJO9qIbEWEIrubzwXbViM9/zO6QWzTjxnjebuEJ0Nc9ZM6rGqKeSsNQZ/\npLe/xEyIxaENn8TEjx+FPjGB7J7dmPjpTxqxRKJFKCfW/GmQuf37MPHTH5fdR1mxppGz5qdcbx/g\nE8npFAAgl9Xw1K/2Y/tTh3HkwERd1qPmdTz92H5k0sGFeTNCYi0gumGCAygevslggS+6TgdURGdD\nc9bqA4m1xsApPmfNdyAsJuyAqZEH/g2xLVuhrFgJM0dBBUR52Jw1v0PLiWJB8MihL34eI/d/B9r4\nmH2fosHrYjxeZt/Us+an0pBr9j42UrZYO35kCs/+5iB+87O9eOn5o2Xvl0rmcWjf2JzWc/LYNJ55\n/CBe3dnafa4k1gJimBYEgSs5wZ5oHJJAPWsEAdhhJDzHU3R/jaEewMbA+8sg/c6ab74aryjgQyFY\n+RwscpSJMjBRxvtEPydJMEvMWbN05/PTKPwclRYNld43ibUCCo6Ri2bPWc5nKRNrQ0u99/Li5d1l\n9/nd//cUHnlgJ6Yns2W3KUcsYTv0k+OtXXpJYi0ghmHRjLUmRCSxRhAuAieQs1ZjyFlrDIU9a96B\nMB+1y6zYwTEfCgEIPuCY6DzMojJIwH59+Z01F2dumjVDrA2U3LdXBklirZjiQeFszhp7zsIR7z2+\nck1f2f2oeft/kc0ED4RhJLrD4Dhgcrx6oddMkPoIiGGaFC7ShLCeNQoYIQhA5Ems1QJ/CRSJtcZQ\n2LPmHWSzAz7eSZ3jFXu7epZCHv7bO7Hvk7fUbf9EfSnuWQPs1xdzxfwwt80yCm9jg7Rn7FunodjF\nrLj9c7YYVlU3xMUyTfv/wPNY9MfXuNv+4fvPwxVv34CunvCs+y0uTQ2CIPCId4Uw1eLOGp0KCIhu\nWDQQuwlxyyB1ChghCIETaM5aDbDgfZ5QGWRj8DtrfNg7kGMHbGJ3j72d46zVU6xld71i/23TBEcV\nNi1HKbHW97arSkb3s+uKnbWy+zYoDbKY0KpViG3ZiuRTT8JIpcH3yK7zHT1zAyKnr3e37emLoKfP\ndst/+d+vQpIFXHTpqSX3a84SJPerH++GZVp4w1vXFZRjdvdGcGjfONS8Dllpzf9Ta666ARimSQOx\nmxA3YIScNYKgMsga4RdoJj2fDcHfsybEYu7l/t9/B4zJSSz6k3fb2ymOWMvXR6z5e+HMTKZgLURr\nUEqsJS64qOS27gwwn1hbfP1HKuybetZKwUft94mZSgE9PTAdR7xSAMmJo9OYnszigjetAV+ikk1T\ny38WW5aFl547BgBYsaYPp6z1Bm6fvmkIHMdBlIRyd296SH0ExDAtKoNsQqgMkiA8BF6ggJEaYBaI\nNfpsaQT+gzoh6gkkqbcXy275JORBO/CBr7OzZjgx48WXidbBDRiRSpcq+svr2LasvDFx4cWIn31u\n+X3TUOySsJMa7D3DRDArWy7FwGAMumaWLVnUtPLfbX4ht2v78YLb1py+CFe+c2NJAdgqkFgLiB0w\n0rr/6HaF0iAJwkPgeHKCaoD/OaQyyMbgP6ir5Ga5Yq1OzpoxOeldTpFYa0VKOWulbgc8UeGWQQqV\n3RhKgywNm7dmZGzhxXpNuUpibcgej/DM4wdL9qdVctbSKS9g6MjBCeglhF0+p+PVF09gbLj13sck\n1gKiG6abPEg0D4JbBkk9awQh8CJ0EmvzxiBnreFwvoARv7NWjFsGWSdnTZ/yiTVy1loSU68s1vyv\nneKeteJEw2JcsUY9awUwp5EJYVYG6Q8OKmbQifJ/7ZVhJKdmvp+LnTXLspCatrfLpPLu9bpmYnpy\n5v137TiOn/3nLvzskV3VPJSmoOKrS9M03HrrrTh69ChUVcX111+Pyy67DADwwx/+EPfddx+++93v\nAgAeeOAB3H///RBFEddffz0uueSS+q9+AaEyyObECxihAyqCEDgeBs2bmjdUBtl4/D1rfBBnrV5i\nbXLKvWym0nX5G0R98eL1A4g1J70QQcXaLPvuVJgwZmWiLGCkUhnkosUJ/K+rN8KygHiXM5LD57AV\nu2WH9o7jRw/uxO++a5Mb63/hJasxMBRHT39kxv7Xb16MSExG36LW6zutKNZ+8IMfoLu7G1/+8pcx\nOTmJq666CpdddhlefvllPPjgg+6TODIygnvvvRcPPfQQ8vk8tm3bhosvvhhyhUbCVoMNxSaaC5qz\nRhAeIifCsGbGURPV4RdoVAbZGAp61mLRstu5c9Zy+bLbzIcCZ43KIFuSWcsgfSW0bnR/wPJG14Gj\nMsgCmNPInkcjY5/o8Ce7lmLF6sJ5a/4EyOIyyIkxe5+6biLjlEF29YSxdGVPyX3Lioi1ZwwGfQhN\nRcW6viuuuAI33XQTAFvdCoKAiYkJfOUrX8Gtt97qbrdjxw5s3boVsiwjHo9jxYoV2LWr9WzGStBQ\n7OaEAkYIwkPgeRIXNcDvTlIPYGPwH/yWm3EFLEDP2vS0d5nKIFuS2QJGCssg2Zy1YD1r3Zdcir63\n/T6NdCiCvX9ZWqaZtnvX+Ej5Ey+l+NGDO93LxWKNuWnhiOSWQUZi5Z27VqbiqYCoM3QylUrhxhtv\nxE033YTbbrsNn/70p6H4rMxUKoV4PF5wv1TAM1ADA/HZN1pgitdkmhZMy0IoJDblejuZgaT9wSor\nEv1viHnT6q+hkCzDtEz09UfBc3TwMFeMlHfwFgrTZ0uj2OP8rPT8Jyd6cQTA2MPfx5p3vA1Sovr/\nVaX9j2tZ97Js5Om10IKMcLY7M7C4B0IJZ2fikFc1FRLt18PUSQWHAUTj4Yr/84ErLq35etsBoS+B\n4wAiCo+BgTjynC3aehb3oy/ge8gyLRw5MOHtU+AL/heW47otXdaDV16wEyBXntKHeCIUaP+t9F6e\n1bc9fvw4PvKRj2Dbtm1YtWoVDh48iM997nPI5/N47bXX8MUvfhEXXHAB0mmvljudTheIt0qMjCTn\nvvo6MDAQn7EmzemHsgyz6dbb6aSS9kHVzj0jeDSh4Kx1Aw1eEdGqlHrvtxqmUwF5YngKEk9lOXNl\nNO25Kcl0tuVfF62KvGQJhFjl96UhxQCOAywLr97zzxh893ur+huzve8zI+Pu5eTxYXottCDZyWmA\n5zE2rYJLzSwTTw57giA9lcLISBLpMfv/nM0b9D+fA+m07XqlJu3nMzliP8cpnYM5x+czlcwV/C8m\nJmy3LptTMXwiCUkWkM2pyOVnDjsvphm/7yuJx4rf5qOjo7j22mtxxx134MILLwQAPPLIIwCAI0eO\n4OMf/zhuu+02jIyM4Ktf/Sry+TxUVcXevXuxbt26Gj6ExsJKYmgodvPBhmK/cnACB08kSawRHY3A\n2yU7hmmQWJsHlAbZHKz6/F/Nuo0Qi+HUf/g6Dv3lXyDzam3aLyzLQnbPboROWQ19etrus+F4aCdP\n1mT/xMKij49B7OkpW6rIEkUBrwwS1Is2L7yAEdazZgsrIVpdGaQkC275o78MMp3K4/jhKQgC/k7D\nMwAAIABJREFUB0kWMDWeQe9AFBzXntkSFV+FX//61zE9PY27774bd999NwDgnnvuQShUaDEODAzg\nmmuuwbZt22BZFm6++eaCMslWxzBtq5XSIJsPxTeRvifePq85gpgLIueItTn0WamGBo7jyoq8tJZB\nRAy37ZehH0qDbC14RcHyWz9TMC9rPmT37MaRL/01Fl3zHhipJIREAkI0itzBg7AMY9aEQKJ5sHQd\n+uQkwqeuLbtNZMNGLPvEn+PIl/9mZsAI/a/nhBvd76RBmixgpMqetVBYKinWHvinZ6CpBpSQiNR0\nHoZhoat3ZgJku1BRrN1+++24/fbbS962bNkyPPDAA+7vV199Na6++urarq5JMAwSa81KX5d34mCw\njd+oBBEEnp+7WPuzX92BgXAfPnPBn8247WRmBF/47d/ij07/A1y85Px5r7PZoTTI1kMIh4FZkuaC\noo+P2RcsE0YyCal/ANKiRcjt2wdtbAzyokU1+TtE/dEnJwDLgtjXV3YbjuOgLF0GoPqAEaI0vFQY\nMGI4rVJsWHZQQmHJnbmm5u3/iWGYyDnhIvmc7s5a627jY0Cq6wsAi4WnodjNh/9/0hewqZQg2hXX\nWTOrE2vTahKGZeBEZrjk7RktAwsWRjJj815jK0BlkJ0NK9kCAJgmhEQC8uAQAEAbplLIVkIbsz+z\npN7yYg3wyvbMKodiE6Vx0yBdZy0DThQLRnIEoavHO66bGM/ANE1MjmUKtukfiuO8N5yCM7Ysnueq\nmxcqxg3Av/9qHwBy1pqViCIik9fRAdVZBFERYY5lkIeTR93LpmXOSJJUBLvEOG+o81xha1BYBknR\n/Z2G6bgA7IBdjCcQP/d8qMeOQlm+vJFLI6qEuaSVnDXA12OlOvP6SKzNC/f51Lw5a3wkUnUZ/evf\nsg4czyGX0XB4/wQmRjMYPm4HgwgCh7f+wQZIkoCzL1pZ2wfQZJBVFIAde+03e16nM6zNyE3v3IRl\nAzG85Vz6EiU6GzYLslpn7dC0J9Yyvqjynx76JXaN74Es2GdD80Z9Bg83G36BRmWQnQdz1pQlS6Es\nX47Iho2QBwex+APXQ+zqbvDqiGowkvaBvZjoqrgdx/PgJMnteww6FJsozYyetXQGQpX9aoBdBnn5\n752BVWv7AQDDx5N4/slD4Djgndeei5VrKovwdoHEWgDWO9PQdx2cmGVLohGsXdaNz7/vPPRSGSTR\n4QicfWChO2Jj1/gejGZnL10cyY66l1OaPSNzIjeJ77/2CJ44/jQUR6ypHeKsURlkZ2Lm85j4n59g\n+te/AgCIff1Y+dkvIH7W2Q1eGTFX2MBrPjT78QEnSa64oJ61+cFErqnrsCwLRjYDvsp+NT9LV/aA\n5zmYpoVMSsWW85ejp699e9SKoVMGAbj0rKV4etcwzl8/2OilEARBlMV11iwDWT2Lu164BwDwj5d+\nqeL9sro3BDqppjAUHcQhpzRycXSow8sgSax1CslnnsbIv33b/b3aMASi+WBijVOCiTVTo561WuCV\nQWp2aIthzEus9fRFcN3HXw9B5HH6xiHwQmf1vZBYC8BpK3rw1x+8AL0UDU8QRBMjMmfNNAoE2Gxk\nda/0ManZ/TqHk0cAACviSyHxIjhwHVQGSWmQrYplWdDHx8CJYtUli24KpMN8Di6J5sDMV+msuWKN\nRffTYfJc8AJGdJhZ+/tFmGdaqyDyBT87CXoVBmSwhz60CYJobtiMNN3UqhJrOd+2KdUugzySOgYA\nWB5fakdbCzKVQRJNz+j3vouJH/83AGDo/R9C4vwLAt9Xn5ws+L3cEGWidaimDJIXJRjOPDBvKDY5\na3OBEwSA52FpGsys3QPKh+k4eq7QJxFBEESbIAl26YlqFIo1y7JnRWb1HL709F14bnhHwf0yvm0n\n89P4m6e+ip2jr0AWZMTlGABAEeQOLYOkNMhWIrb1bIRWrwEAZF55qar76lOTs29EtBSeWJu9MqrQ\nWWNlkORpzBVOFGHpOgzHWeNrNAexEyGxRhAE0SbIvBMEYmoFpY2sfPH54R04mDyMb754X8H9cnoO\nvaEebOo/E6d0rcBhx1WLSzFv34LcQWWQ9UuDHM6M4J6d38Jkfqqm+yVswmvXYvmnbgUny8gfPFDV\nfYudNaL1MfP2ZxYfsGeNibXoho2IbtkKZeWqei6vreFEyS6DzDBnjcTaXKFTBhX49Y7j6EkoOHNV\nb6OXQhAEMSuScxZYMzTopu5en9LSCIkhqKY24z6WZSFr5DAYHcAHN/0pLMuCwAkwLAMx2YtaVgQF\nSTVd/wfRBBSUQZq1FWuvTe7HCyMvYlP/mTh/MaUM1gNOEKAsW47cwQMwNRW8FGwQrzE1CbGvD/Lg\nkOvOlSJ36CDS218ALAuJCy+GNDBQq6UTNcbM5cDJcqCgEDsN0k4vDJ2yGks/etMCrLB94STRKYN0\nnDXqAZ0z5KyVwbIsfOvRXXjkNwcavRSCIIhA+J21XFHCIwCktcyM+6imBtMyERLtM88cx7lz1fzO\nmuI4a6yksp0pToO0LAsvjLyIlDZ/serNrOuMktJGoSxfDhgGtOHhQNtbpgl9ehpidw+WffwT6L/q\nD8pum3rmaYw9/H2M/eA/cPLb36rVkok6YOay4JVg4XBugqE+86QWUT1sFILh9KwJ1LM2Z0islUHV\nTeiGBYmaSwmCaBFYz5pWFDDCxNpkbmaZFyuXDAtemRDP2bHIMclz1mRBhgWrwLFrV/xiTRFlvDy+\nG/fs/Bb+8YVvznvfIXcMQmeUlDYKsduejxq0tJH1KMmLZh/R033pZeAcAZDdswdWjd1XonaYuVyg\ncBGgMG6emD+cKMLSvDRIKoOcOyTWypDL2wckIZnEGkEQrYHMlw4YSTmO2rhPrPlDRwAgLPrFGu/8\n9GbZdNKsNcPpWbty1eX449Pf6Q4WP+SMM5gPCom1BYHF9gcVa7wkYeUdf4GBd/3R7Pvu7sHKOz6P\nyJkbYOVz0MZGZ70P0RisfD6wWONJrNUUu2fNlwZJZZBzhnrWypBV7S/rsEJijSCI1oCV2GlFASOq\naQusKXXavS5v5BESQz6x5p31FDj7c8/fu6W45Xt5xOA5bu2I4Tgli2NDbhpmrVBE+3nMkVirK0J3\nFwC7Dy0oytJlgbeVBwcx9N7rkH5pJ6S+/qrXR9Qfy7LsnrUA4SKALS4AEmu1gqVBmpnazFnrZMhZ\nK0PWddZIzxIE0RqwOWuqoRY4a6x08bIVbwQH2y1j/VelnDXWvyby3skqpYN6rVgaJBOttcQtg9RJ\nrNUTtwxyqn6pm2J3N7oufj3NY2tSLFUFLIvKIBsE76RrGllKg5wv9AlTBlYGGVZIrBEE0Rp4zpqO\nrOGJNc2wP88uXHwOLlvxBgAz+9gSctzd/v0b/gTre9fhd1e/1b2uR7HLykay7V/yxRxFgav9V6Ti\n9AZ2guhtJGKX7azpkxMNXgnRKKoZiA14Ys0ksVYT4uddgMRFFyO2cTNiW8+G2NvX6CW1LKREypBj\nZZDUs0YQRIsguT1rKpZGF+PlsVcBALovsp+FhjBn7WR2BAAwGPXix4eig/jolusK9r08vhQAcCh5\nFJsHNtTpETQHRh2dNeZQUhlkfRHiCYDj6uqsMfSpKUw/8Ti6L3uz2/dENB4+EobUP4DwqWsDbU/O\nWm3pvuRS4JJLAQDx885v8GpaG3LWypBVnTJIctYIgmgRZF8a5FWnXolPnnOD87uX4BhxetMymt1H\nMJyxnbJF4cqzophYO5w8WttFNyGG6Yi1OpS3yYIEDhxyVAZZVzieh7x02YKIp9Rzz2L0wQeQeXFn\n3f8WERxekrHqr7+EnsvfEnB7EmtEc0JKpAzZvP1lTWmQBEG0CpIvDdL/u1+shSVHrDkBJCOZUUTE\nMKJS5aSumBxFl5zAsdSJmq+72WDR/XwdnDWe4yELElRy1urOij+/FahDKWsxfNR+7+gT44G2V08c\nByfLkJyyMMuyoB49AiHRBTGRqNs6OxHOl2g767Y0Z41oUkislSGnUs8aQRCthcx7zhoAiE7giL8M\nkjlrLC1yMj+NxdFFgQ5qwmKoJoOhm5169qwBdsgIlUHWHz4ULNDAsqyqDuqLcccEBCi5zO7Zg8N3\nfhEAsOb/3AUhHkfyt7/BiW/eA04J4dS77qbAkgbBifbnJTlrRLNBnwhlYM5amNIgCYJoEQReAM/x\nrlhj6ZAFZZBFztq209+Od657W6D9i7zo7rudqWfPGgAoIom1RmBqGvbf/ucY/fcH3euyx09g3599\nDJO//MWc9+uJtdnHBGijI97lcXt+X/6oXVps5XMwksk5r4OYHxQwQjQrJNbKkHN71qgMkiCI1kHm\n5YplkMU9a2cPbsbKxPJA+xZ5EbrTz9XOuGKNr93n/2uT+/F/nvsaUloaiqBQGmQDyO5+FdqJEwUH\n4wf+5VswpqYgxOY+T0/sZsmTs4s1llAIAEbKTmQ1fI5cEMFH1AcKGCGaFRJrZaCeNYIgWhFJEAOV\nQWZ8Q7ODIvICDMtwe7raFcPtWbO/Ii1Y897nS2O78NrkfgxnRhASFKiG2vbPY7OR3rEdABDbtBkA\nYGTSGH/qaSgrViJ21tlz3i8fCoNTQoEGcJt5n1hL22LNL/KCCD6iPpBYI5oVEmtlmE7bJSqxMMXw\nEgTROhQ6azPLIENiCBw411mrBubUGW3urpksDZKJNWv+Yi3nDB8PCSGERGcwNpVCLhiWZSG9/QXw\noRDCa9cBADIvvQSYJmJbz5pXzxpgu2vayAjGf/SfMLJZaKMjGH34+8jseqVgO7+zZjrOmt9NCyL4\niPpAaZBEs0JirQwnxrPoiskIUc8aQRAtRE+oyxUDrIftSPIYJpzh1zzHIySG3ICRahCdskC/+GtH\nvIARwfl9/uI060T1h0QF4aJSVKL+qMePQxsdQeTMDW6QhDY+BvD8vFw1hrx4CcxcDqP//iCSTz+J\n5FNPYvyHD+Pkt/6lYLuCMsi0HdbjF2sLMReOKA0LpDEymQavhCAKIbFWAk03MD6dw1BP5ShrgiCI\nZuNDm96Dj239kPu7yItI6xl8e5cXqhARw3Mrg+Scskqr3cVaYc9aLZxE5qKFhNC8SlGJuZE/fBAA\nENuy1b2u5/K34Oyv/wOUpcvmvf/F130Qi675UwCAPjqKnrf+DgDASBUGhhSUQaZSMDUVZjptD/EG\nlUE2EmnAnjWpDZ9s8EoIohASayUYnsjCAjDYS2KNIIjWIiyGEZOj7u+lSvgi0hzFmtsD1+5irbBn\nrRa9ZW4ZpKjMCHkh6k/srHOw5CM3IH7+he51nCAgNDhYk/3zoRCiGzYBALSxUXCCgPDadTCz2YL3\noFUUMGI6Lk7olFMAAKbTx0YsPGJvHzhRhHqSxBrRXFCNXwmOj9kfnkMk1giCaHFY2Ih/6LU/4IKv\nYpaY1CFizetZq10ZZM7IQeYl8ByPiPO/IGdt4eAlCbGt8y93rITY3Q1wHPRxezg2Hw4DlgUrnwPn\nlNgVlkGmICS6MHD1HyGyYQOim7dCWb6irmskysPxPKRFg9BOnpj37D2CqCUk1krwysEJAMApi+MN\nXglBEERtiEqe26YIMgBANTS3vy0InrPW3gEj3pw13vm9Fs5aHiExBGDmYHKiPeAEAWJPD7Qxe34a\nH/Z6oPgiscaJIoxUChzHoectbwUAKEuWNmDVhB95cAjqsaMwpqfc+XkE0WhIrBVhWRa27x1FNCTi\n1GVdjV4OQRBETfA7a7Ij1vKGOiex1u6DsWcEjNRAnGaNHMJMrEnUs9auiL19yO3bC8swwIft95yZ\n9f7PZj4PTgkhdtZZEJ0+NaJ5iJ1zLrTxMVdcE0QzQGKtiNHJHMan8zj7tAEIPLX0EQTRHvjFmiLY\nAk2tcjBz5zhrte9Zy+t59Cj2mfpq0yD3TR0AAKzuWjXvdRD1JXzqWmgnTwKW5TprZsYn1nI58KEQ\nFr/vA41aIlGBxPkXIHH+BY1eBkEUQGqkiGMjdnPvkr7oLFsSBEG0DjHRL9aYs1bdnK+OCRhxxCjv\nlkHOT5wapgHV1BByRHK1aZD/+tL9+M6uh+a1BmJh6H/7O3HKl/4OnChCiNjvOSP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4Qcx4c2vQfLYkuaunxTERTozuBumrVGVIPUa/eX6RPjtlhzRVF1g7JNTQMAcLOkQBIEMTsVyyCv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\nj+5cTIRO2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f779098f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i['corn'].plot_contracts('2000','2002', panama=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aren't interested in the absolute price, only the relative price (our moving averages effectively work on the first derivative- they are trying to find the trend). So what we can do is just move each contract up/down, so that it joins where the previous contract left off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:50:22.929101",
     "start_time": "2017-07-22T10:50:21.417393"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7789d75f98>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tqXb7jTVlyhrLpvqq0Q35HovsUFaZGh8wq6ZMs2py3989ytlwoxFPZAIsIQ4A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f77525e6438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i['corn'].panama_prices().loc['2000':'2002'].plot(title='Panama prices for Corn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at this, we can see the absolute price on the left no longer makes any sense (it's negative). But this doesn't concern us; once we apply the moving average to this, it becomes irrelvant. In this way, we can now measure the trend across contracts.\n",
    "\n",
    "There are various more advanced time-weighted versions that can preserve the price, but we don't need these. The simple method we use here is known as **Panama stitching**.\n",
    "\n",
    "## Forecasts\n",
    "\n",
    "We apply our forecasts to this data use `Instrument.forecasts()`.\n",
    "\n",
    "For Corn, we can see we have several variations of the EWMAC rule, and another rule, carry_next (to be discussed in a later chapter)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:50:24.824757",
     "start_time": "2017-07-22T10:50:22.932231"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ewmac8</th>\n",
       "      <th>ewmac16</th>\n",
       "      <th>ewmac32</th>\n",
       "      <th>ewmac64</th>\n",
       "      <th>carry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993-11-26</th>\n",
       "      <td>7.564933</td>\n",
       "      <td>10.031147</td>\n",
       "      <td>9.367099</td>\n",
       "      <td>6.456318</td>\n",
       "      <td>0.799245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-29</th>\n",
       "      <td>7.342984</td>\n",
       "      <td>10.091502</td>\n",
       "      <td>9.623840</td>\n",
       "      <td>6.709447</td>\n",
       "      <td>0.874910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-11-30</th>\n",
       "      <td>6.963521</td>\n",
       "      <td>10.042939</td>\n",
       "      <td>9.819248</td>\n",
       "      <td>6.934971</td>\n",
       "      <td>0.943919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-01</th>\n",
       "      <td>6.805610</td>\n",
       "      <td>10.079599</td>\n",
       "      <td>10.049711</td>\n",
       "      <td>7.178643</td>\n",
       "      <td>1.007835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-02</th>\n",
       "      <td>6.835381</td>\n",
       "      <td>10.201739</td>\n",
       "      <td>10.318946</td>\n",
       "      <td>7.442906</td>\n",
       "      <td>1.069332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-03</th>\n",
       "      <td>6.622991</td>\n",
       "      <td>10.192886</td>\n",
       "      <td>10.518674</td>\n",
       "      <td>7.675724</td>\n",
       "      <td>1.127297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-06</th>\n",
       "      <td>6.425972</td>\n",
       "      <td>10.173868</td>\n",
       "      <td>10.706501</td>\n",
       "      <td>7.903860</td>\n",
       "      <td>1.184627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-07</th>\n",
       "      <td>6.361241</td>\n",
       "      <td>10.209867</td>\n",
       "      <td>10.916001</td>\n",
       "      <td>8.143450</td>\n",
       "      <td>1.242661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-08</th>\n",
       "      <td>6.346675</td>\n",
       "      <td>10.267330</td>\n",
       "      <td>11.132206</td>\n",
       "      <td>8.387592</td>\n",
       "      <td>1.305190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-09</th>\n",
       "      <td>6.274236</td>\n",
       "      <td>10.292028</td>\n",
       "      <td>11.328124</td>\n",
       "      <td>8.623237</td>\n",
       "      <td>1.366862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-10</th>\n",
       "      <td>6.136807</td>\n",
       "      <td>10.275887</td>\n",
       "      <td>11.498529</td>\n",
       "      <td>8.847452</td>\n",
       "      <td>1.427900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-13</th>\n",
       "      <td>5.740622</td>\n",
       "      <td>10.110323</td>\n",
       "      <td>11.586025</td>\n",
       "      <td>9.031759</td>\n",
       "      <td>1.487188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-14</th>\n",
       "      <td>5.421199</td>\n",
       "      <td>9.957585</td>\n",
       "      <td>11.669079</td>\n",
       "      <td>9.212955</td>\n",
       "      <td>1.548295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-15</th>\n",
       "      <td>5.209525</td>\n",
       "      <td>9.841828</td>\n",
       "      <td>11.761099</td>\n",
       "      <td>9.397539</td>\n",
       "      <td>1.611668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-16</th>\n",
       "      <td>4.984706</td>\n",
       "      <td>9.707200</td>\n",
       "      <td>11.835027</td>\n",
       "      <td>9.572493</td>\n",
       "      <td>1.680872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-17</th>\n",
       "      <td>4.871712</td>\n",
       "      <td>9.620565</td>\n",
       "      <td>11.925034</td>\n",
       "      <td>9.754214</td>\n",
       "      <td>1.754126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-20</th>\n",
       "      <td>4.865625</td>\n",
       "      <td>9.588017</td>\n",
       "      <td>12.036404</td>\n",
       "      <td>9.945615</td>\n",
       "      <td>1.828306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-21</th>\n",
       "      <td>4.890369</td>\n",
       "      <td>9.576384</td>\n",
       "      <td>12.154274</td>\n",
       "      <td>10.139849</td>\n",
       "      <td>1.902967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-22</th>\n",
       "      <td>4.842585</td>\n",
       "      <td>9.531766</td>\n",
       "      <td>12.251740</td>\n",
       "      <td>10.323894</td>\n",
       "      <td>1.974987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-23</th>\n",
       "      <td>4.695090</td>\n",
       "      <td>9.433642</td>\n",
       "      <td>12.317095</td>\n",
       "      <td>10.491666</td>\n",
       "      <td>2.044812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-27</th>\n",
       "      <td>4.595133</td>\n",
       "      <td>9.353760</td>\n",
       "      <td>12.385583</td>\n",
       "      <td>10.659853</td>\n",
       "      <td>2.113557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-28</th>\n",
       "      <td>4.531254</td>\n",
       "      <td>9.289768</td>\n",
       "      <td>12.456907</td>\n",
       "      <td>10.828401</td>\n",
       "      <td>2.183229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-29</th>\n",
       "      <td>4.470916</td>\n",
       "      <td>9.226867</td>\n",
       "      <td>12.524210</td>\n",
       "      <td>10.994037</td>\n",
       "      <td>2.250259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-12-30</th>\n",
       "      <td>4.366616</td>\n",
       "      <td>9.139670</td>\n",
       "      <td>12.574576</td>\n",
       "      <td>11.150390</td>\n",
       "      <td>2.317994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-03</th>\n",
       "      <td>4.278962</td>\n",
       "      <td>9.057468</td>\n",
       "      <td>12.622393</td>\n",
       "      <td>11.304233</td>\n",
       "      <td>2.382864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-04</th>\n",
       "      <td>4.086247</td>\n",
       "      <td>8.916192</td>\n",
       "      <td>12.634910</td>\n",
       "      <td>11.439498</td>\n",
       "      <td>2.444647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-05</th>\n",
       "      <td>3.985131</td>\n",
       "      <td>8.814010</td>\n",
       "      <td>12.660837</td>\n",
       "      <td>11.579462</td>\n",
       "      <td>2.504458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-06</th>\n",
       "      <td>3.928621</td>\n",
       "      <td>8.732901</td>\n",
       "      <td>12.692561</td>\n",
       "      <td>11.720686</td>\n",
       "      <td>2.567426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-07</th>\n",
       "      <td>3.787061</td>\n",
       "      <td>8.606463</td>\n",
       "      <td>12.696713</td>\n",
       "      <td>11.846942</td>\n",
       "      <td>2.630429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-10</th>\n",
       "      <td>3.419503</td>\n",
       "      <td>8.352615</td>\n",
       "      <td>12.629293</td>\n",
       "      <td>11.936163</td>\n",
       "      <td>2.697927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>-0.766698</td>\n",
       "      <td>-2.777330</td>\n",
       "      <td>-7.586197</td>\n",
       "      <td>-13.415782</td>\n",
       "      <td>-14.395148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>-0.985386</td>\n",
       "      <td>-2.852889</td>\n",
       "      <td>-7.577158</td>\n",
       "      <td>-13.417825</td>\n",
       "      <td>-14.297700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>-0.942454</td>\n",
       "      <td>-2.805344</td>\n",
       "      <td>-7.508810</td>\n",
       "      <td>-13.389632</td>\n",
       "      <td>-14.200921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17</th>\n",
       "      <td>-0.566866</td>\n",
       "      <td>-2.576314</td>\n",
       "      <td>-7.346026</td>\n",
       "      <td>-13.312159</td>\n",
       "      <td>-14.104810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <td>-0.379534</td>\n",
       "      <td>-2.422038</td>\n",
       "      <td>-7.215010</td>\n",
       "      <td>-13.247939</td>\n",
       "      <td>-14.008031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <td>-0.166509</td>\n",
       "      <td>-2.243145</td>\n",
       "      <td>-7.067238</td>\n",
       "      <td>-13.172903</td>\n",
       "      <td>-13.910583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>-0.008621</td>\n",
       "      <td>-2.082035</td>\n",
       "      <td>-6.923933</td>\n",
       "      <td>-13.097587</td>\n",
       "      <td>-13.811796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-23</th>\n",
       "      <td>0.035812</td>\n",
       "      <td>-1.974905</td>\n",
       "      <td>-6.804719</td>\n",
       "      <td>-13.032125</td>\n",
       "      <td>-13.711449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <td>0.494043</td>\n",
       "      <td>-1.647006</td>\n",
       "      <td>-6.569121</td>\n",
       "      <td>-12.905424</td>\n",
       "      <td>-13.608203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-25</th>\n",
       "      <td>0.727469</td>\n",
       "      <td>-1.411542</td>\n",
       "      <td>-6.371493</td>\n",
       "      <td>-12.793960</td>\n",
       "      <td>-13.502281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>0.982944</td>\n",
       "      <td>-1.154143</td>\n",
       "      <td>-6.156301</td>\n",
       "      <td>-12.670230</td>\n",
       "      <td>-13.393460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <td>1.345809</td>\n",
       "      <td>-0.828001</td>\n",
       "      <td>-5.898632</td>\n",
       "      <td>-12.521261</td>\n",
       "      <td>-13.282186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>1.782251</td>\n",
       "      <td>-0.444249</td>\n",
       "      <td>-5.601921</td>\n",
       "      <td>-12.348056</td>\n",
       "      <td>-13.167344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>2.053450</td>\n",
       "      <td>-0.127184</td>\n",
       "      <td>-5.328859</td>\n",
       "      <td>-12.181898</td>\n",
       "      <td>-13.050718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>2.223942</td>\n",
       "      <td>0.144431</td>\n",
       "      <td>-5.071316</td>\n",
       "      <td>-12.019189</td>\n",
       "      <td>-12.932754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-02</th>\n",
       "      <td>2.247517</td>\n",
       "      <td>0.338006</td>\n",
       "      <td>-4.848193</td>\n",
       "      <td>-11.869941</td>\n",
       "      <td>-12.813899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-05</th>\n",
       "      <td>1.950306</td>\n",
       "      <td>0.349573</td>\n",
       "      <td>-4.716589</td>\n",
       "      <td>-11.763985</td>\n",
       "      <td>-12.694151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <td>2.085773</td>\n",
       "      <td>0.561874</td>\n",
       "      <td>-4.484710</td>\n",
       "      <td>-11.605382</td>\n",
       "      <td>-12.572619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-07</th>\n",
       "      <td>2.275062</td>\n",
       "      <td>0.805048</td>\n",
       "      <td>-4.233004</td>\n",
       "      <td>-11.433236</td>\n",
       "      <td>-12.448858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-08</th>\n",
       "      <td>2.406404</td>\n",
       "      <td>1.022827</td>\n",
       "      <td>-3.989634</td>\n",
       "      <td>-11.261579</td>\n",
       "      <td>-12.322643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-09</th>\n",
       "      <td>2.209473</td>\n",
       "      <td>1.065344</td>\n",
       "      <td>-3.833502</td>\n",
       "      <td>-11.130852</td>\n",
       "      <td>-12.193753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-12</th>\n",
       "      <td>2.485759</td>\n",
       "      <td>1.339710</td>\n",
       "      <td>-3.559943</td>\n",
       "      <td>-10.938473</td>\n",
       "      <td>-12.061294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <td>2.559424</td>\n",
       "      <td>1.518582</td>\n",
       "      <td>-3.330037</td>\n",
       "      <td>-10.764318</td>\n",
       "      <td>-11.927052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-14</th>\n",
       "      <td>2.626138</td>\n",
       "      <td>1.691515</td>\n",
       "      <td>-3.101067</td>\n",
       "      <td>-10.587521</td>\n",
       "      <td>-11.790357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-15</th>\n",
       "      <td>2.734234</td>\n",
       "      <td>1.884204</td>\n",
       "      <td>-2.859961</td>\n",
       "      <td>-10.401490</td>\n",
       "      <td>-11.650762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-16</th>\n",
       "      <td>2.729028</td>\n",
       "      <td>2.016712</td>\n",
       "      <td>-2.647508</td>\n",
       "      <td>-10.226832</td>\n",
       "      <td>-11.510053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>2.593748</td>\n",
       "      <td>2.071814</td>\n",
       "      <td>-2.474972</td>\n",
       "      <td>-10.069889</td>\n",
       "      <td>-11.369567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-21</th>\n",
       "      <td>2.507335</td>\n",
       "      <td>2.136898</td>\n",
       "      <td>-2.299863</td>\n",
       "      <td>-9.909811</td>\n",
       "      <td>-11.227966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-22</th>\n",
       "      <td>2.481471</td>\n",
       "      <td>2.223011</td>\n",
       "      <td>-2.115930</td>\n",
       "      <td>-9.743389</td>\n",
       "      <td>-11.086588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-23</th>\n",
       "      <td>2.429153</td>\n",
       "      <td>2.288608</td>\n",
       "      <td>-1.943836</td>\n",
       "      <td>-9.581010</td>\n",
       "      <td>-10.946102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6106 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ewmac8    ewmac16    ewmac32    ewmac64      carry\n",
       "date                                                            \n",
       "1993-11-26  7.564933  10.031147   9.367099   6.456318   0.799245\n",
       "1993-11-29  7.342984  10.091502   9.623840   6.709447   0.874910\n",
       "1993-11-30  6.963521  10.042939   9.819248   6.934971   0.943919\n",
       "1993-12-01  6.805610  10.079599  10.049711   7.178643   1.007835\n",
       "1993-12-02  6.835381  10.201739  10.318946   7.442906   1.069332\n",
       "1993-12-03  6.622991  10.192886  10.518674   7.675724   1.127297\n",
       "1993-12-06  6.425972  10.173868  10.706501   7.903860   1.184627\n",
       "1993-12-07  6.361241  10.209867  10.916001   8.143450   1.242661\n",
       "1993-12-08  6.346675  10.267330  11.132206   8.387592   1.305190\n",
       "1993-12-09  6.274236  10.292028  11.328124   8.623237   1.366862\n",
       "1993-12-10  6.136807  10.275887  11.498529   8.847452   1.427900\n",
       "1993-12-13  5.740622  10.110323  11.586025   9.031759   1.487188\n",
       "1993-12-14  5.421199   9.957585  11.669079   9.212955   1.548295\n",
       "1993-12-15  5.209525   9.841828  11.761099   9.397539   1.611668\n",
       "1993-12-16  4.984706   9.707200  11.835027   9.572493   1.680872\n",
       "1993-12-17  4.871712   9.620565  11.925034   9.754214   1.754126\n",
       "1993-12-20  4.865625   9.588017  12.036404   9.945615   1.828306\n",
       "1993-12-21  4.890369   9.576384  12.154274  10.139849   1.902967\n",
       "1993-12-22  4.842585   9.531766  12.251740  10.323894   1.974987\n",
       "1993-12-23  4.695090   9.433642  12.317095  10.491666   2.044812\n",
       "1993-12-27  4.595133   9.353760  12.385583  10.659853   2.113557\n",
       "1993-12-28  4.531254   9.289768  12.456907  10.828401   2.183229\n",
       "1993-12-29  4.470916   9.226867  12.524210  10.994037   2.250259\n",
       "1993-12-30  4.366616   9.139670  12.574576  11.150390   2.317994\n",
       "1994-01-03  4.278962   9.057468  12.622393  11.304233   2.382864\n",
       "1994-01-04  4.086247   8.916192  12.634910  11.439498   2.444647\n",
       "1994-01-05  3.985131   8.814010  12.660837  11.579462   2.504458\n",
       "1994-01-06  3.928621   8.732901  12.692561  11.720686   2.567426\n",
       "1994-01-07  3.787061   8.606463  12.696713  11.846942   2.630429\n",
       "1994-01-10  3.419503   8.352615  12.629293  11.936163   2.697927\n",
       "...              ...        ...        ...        ...        ...\n",
       "2018-01-11 -0.766698  -2.777330  -7.586197 -13.415782 -14.395148\n",
       "2018-01-12 -0.985386  -2.852889  -7.577158 -13.417825 -14.297700\n",
       "2018-01-16 -0.942454  -2.805344  -7.508810 -13.389632 -14.200921\n",
       "2018-01-17 -0.566866  -2.576314  -7.346026 -13.312159 -14.104810\n",
       "2018-01-18 -0.379534  -2.422038  -7.215010 -13.247939 -14.008031\n",
       "2018-01-19 -0.166509  -2.243145  -7.067238 -13.172903 -13.910583\n",
       "2018-01-22 -0.008621  -2.082035  -6.923933 -13.097587 -13.811796\n",
       "2018-01-23  0.035812  -1.974905  -6.804719 -13.032125 -13.711449\n",
       "2018-01-24  0.494043  -1.647006  -6.569121 -12.905424 -13.608203\n",
       "2018-01-25  0.727469  -1.411542  -6.371493 -12.793960 -13.502281\n",
       "2018-01-26  0.982944  -1.154143  -6.156301 -12.670230 -13.393460\n",
       "2018-01-29  1.345809  -0.828001  -5.898632 -12.521261 -13.282186\n",
       "2018-01-30  1.782251  -0.444249  -5.601921 -12.348056 -13.167344\n",
       "2018-01-31  2.053450  -0.127184  -5.328859 -12.181898 -13.050718\n",
       "2018-02-01  2.223942   0.144431  -5.071316 -12.019189 -12.932754\n",
       "2018-02-02  2.247517   0.338006  -4.848193 -11.869941 -12.813899\n",
       "2018-02-05  1.950306   0.349573  -4.716589 -11.763985 -12.694151\n",
       "2018-02-06  2.085773   0.561874  -4.484710 -11.605382 -12.572619\n",
       "2018-02-07  2.275062   0.805048  -4.233004 -11.433236 -12.448858\n",
       "2018-02-08  2.406404   1.022827  -3.989634 -11.261579 -12.322643\n",
       "2018-02-09  2.209473   1.065344  -3.833502 -11.130852 -12.193753\n",
       "2018-02-12  2.485759   1.339710  -3.559943 -10.938473 -12.061294\n",
       "2018-02-13  2.559424   1.518582  -3.330037 -10.764318 -11.927052\n",
       "2018-02-14  2.626138   1.691515  -3.101067 -10.587521 -11.790357\n",
       "2018-02-15  2.734234   1.884204  -2.859961 -10.401490 -11.650762\n",
       "2018-02-16  2.729028   2.016712  -2.647508 -10.226832 -11.510053\n",
       "2018-02-20  2.593748   2.071814  -2.474972 -10.069889 -11.369567\n",
       "2018-02-21  2.507335   2.136898  -2.299863  -9.909811 -11.227966\n",
       "2018-02-22  2.481471   2.223011  -2.115930  -9.743389 -11.086588\n",
       "2018-02-23  2.429153   2.288608  -1.943836  -9.581010 -10.946102\n",
       "\n",
       "[6106 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i['corn'].forecasts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to find a way to combine these forecasts into a single forecast that we can use to make a position. In our case, we will use a weighted mean.\n",
    "\n",
    "To determine the weights, we use bootstrapping.\n",
    "\n",
    "## Bootstrapping rule weights\n",
    "\n",
    "In order to find the best weights for the rules, we take samples, with replacement, of continuous sections of the price history, and we see what combination of rules works best for that sample (we optimize the Sharpe ratio, to be discussed later). We repeat this several hundred times, until we have stable weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:50:37.589159",
     "start_time": "2017-07-22T10:50:24.827299"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap corn starting\n",
      "{'annual_vol': '0.1078',\n",
      " 'avg_drawdown': -0.11463082060204904,\n",
      " 'avg_return_to_drawdown': 0.21413153824003053,\n",
      " 'calmar': 0.0644950642006962,\n",
      " 'cap': 500000,\n",
      " 'gross_sharpe': 0.25176313949977686,\n",
      " 'sharpe': 0.19987177297132627,\n",
      " 'sortino': 0.25094556883354119,\n",
      " 'time_in_drawdown': '0.8516',\n",
      " 'worst_drawdown': -0.38058841012780603}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ewmac8     1.008093\n",
       "ewmac16    0.882875\n",
       "ewmac32    0.884973\n",
       "ewmac64    1.356357\n",
       "carry      0.867702\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i['corn'].bootstrap()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Things to note:\n",
    "* The faster EWMAC variations seem to get much lower weights. This makes sense, as they cost more to trade, and our system accounts for trading costs.\n",
    "* We really want uncorrelated rules. Having two very similar rules misses the point.\n",
    "\n",
    "In reality, we would **never** use these weights, as they've been fitted to a single instrument. Instead, we would run this process on many different instruments, spanning hundreds of years of history, and then test the generated weights on a set of test data that wasn't used for the original bootstrapping, to check we haven't overfitted.\n",
    "\n",
    "Let's use our systems predefined weights for the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:50:37.629216",
     "start_time": "2017-07-22T10:50:37.591202"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'buy_and_hold': 1,\n",
       " 'carry': 1.0761308295764749,\n",
       " 'ewmac16': 0.9456580263849846,\n",
       " 'ewmac32': 0.9740415156551601,\n",
       " 'ewmac64': 1.037272611390585,\n",
       " 'ewmac8': 0.9668970627792554,\n",
       " 'sell_and_hold': 1}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config.strategy.rule_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forecast * weights = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:50:37.687794",
     "start_time": "2017-07-22T10:50:37.631219"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1993-11-26     9.918410\n",
       "1993-11-29    10.047668\n",
       "1993-11-30    10.072994\n",
       "1993-12-01    10.199734\n",
       "1993-12-02    10.420929\n",
       "1993-12-03    10.505216\n",
       "1993-12-06    10.585983\n",
       "1993-12-07    10.730055\n",
       "1993-12-08    10.899273\n",
       "1993-12-09    11.033996\n",
       "1993-12-10    11.127585\n",
       "1993-12-13    11.068446\n",
       "1993-12-14    11.033230\n",
       "1993-12-15    11.043588\n",
       "1993-12-16    11.038567\n",
       "1993-12-17    11.087031\n",
       "1993-12-20    11.190705\n",
       "1993-12-21    11.311969\n",
       "1993-12-22    11.393358\n",
       "1993-12-23    11.416230\n",
       "1993-12-27    11.458517\n",
       "1993-12-28    11.516816\n",
       "1993-12-29    11.573539\n",
       "1993-12-30    11.603347\n",
       "1994-01-03    11.636904\n",
       "1994-01-04    11.606941\n",
       "1994-01-05    11.618855\n",
       "1994-01-06    11.652513\n",
       "1994-01-07    11.636565\n",
       "1994-01-10    11.489579\n",
       "                ...    \n",
       "2018-01-11   -11.885550\n",
       "2018-01-12   -11.936404\n",
       "2018-01-16   -11.851634\n",
       "2018-01-17   -11.578548\n",
       "2018-01-18   -11.393359\n",
       "2018-01-19   -11.185541\n",
       "2018-01-22   -10.999277\n",
       "2018-01-23   -10.870119\n",
       "2018-01-24   -10.507253\n",
       "2018-01-25   -10.249468\n",
       "2018-01-26    -9.969462\n",
       "2018-01-29    -9.618643\n",
       "2018-01-30    -9.210759\n",
       "2018-01-31    -8.877315\n",
       "2018-02-01    -8.590565\n",
       "2018-02-02    -8.381547\n",
       "2018-02-05    -8.354839\n",
       "2018-02-06    -8.102272\n",
       "2018-02-07    -7.815047\n",
       "2018-02-08    -7.553311\n",
       "2018-02-09    -7.471610\n",
       "2018-02-12    -7.135437\n",
       "2018-02-13    -6.901688\n",
       "2018-02-14    -6.670273\n",
       "2018-02-15    -6.414208\n",
       "2018-02-16    -6.218867\n",
       "2018-02-20    -6.099505\n",
       "2018-02-21    -5.961288\n",
       "2018-02-22    -5.795411\n",
       "2018-02-23    -5.647801\n",
       "Length: 6106, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i['corn'].weighted_forecast()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-19T15:28:54.586172",
     "start_time": "2017-06-19T15:28:53.696856"
    }
   },
   "source": [
    "Putting this all together, we can calculate the position. This is based on an account size of £500,000 GBP. Corn is priced in USD. In `position()`, we convert our instrument return volatility to GBP, so that we can have a daily volatility target also in GBP.\n",
    "\n",
    "Note that the contracts numbers are integers. We round these as we cannot own fractional contracts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:50:37.739699",
     "start_time": "2017-07-22T10:50:37.689871"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1993-11-26    40.0\n",
       "1993-11-29    41.0\n",
       "1993-11-30    42.0\n",
       "1993-12-01    43.0\n",
       "1993-12-02    45.0\n",
       "1993-12-03    46.0\n",
       "1993-12-06    48.0\n",
       "1993-12-07    50.0\n",
       "1993-12-08    52.0\n",
       "1993-12-09    54.0\n",
       "1993-12-10    56.0\n",
       "1993-12-13    54.0\n",
       "1993-12-14    55.0\n",
       "1993-12-15    57.0\n",
       "1993-12-16    58.0\n",
       "1993-12-17    60.0\n",
       "1993-12-20    62.0\n",
       "1993-12-21    65.0\n",
       "1993-12-22    67.0\n",
       "1993-12-23    69.0\n",
       "1993-12-27    71.0\n",
       "1993-12-28    73.0\n",
       "1993-12-29    75.0\n",
       "1993-12-30    77.0\n",
       "1994-01-03    79.0\n",
       "1994-01-04    80.0\n",
       "1994-01-05    82.0\n",
       "1994-01-06    85.0\n",
       "1994-01-07    86.0\n",
       "1994-01-10    81.0\n",
       "              ... \n",
       "2018-01-11   -65.0\n",
       "2018-01-12   -65.0\n",
       "2018-01-16   -65.0\n",
       "2018-01-17   -60.0\n",
       "2018-01-18   -60.0\n",
       "2018-01-19   -61.0\n",
       "2018-01-22   -62.0\n",
       "2018-01-23   -63.0\n",
       "2018-01-24   -55.0\n",
       "2018-01-25   -55.0\n",
       "2018-01-26   -55.0\n",
       "2018-01-29   -53.0\n",
       "2018-01-30   -51.0\n",
       "2018-01-31   -51.0\n",
       "2018-02-01   -51.0\n",
       "2018-02-02   -50.0\n",
       "2018-02-05   -47.0\n",
       "2018-02-06   -42.0\n",
       "2018-02-07   -41.0\n",
       "2018-02-08   -41.0\n",
       "2018-02-09   -39.0\n",
       "2018-02-12   -34.0\n",
       "2018-02-13   -34.0\n",
       "2018-02-14   -33.0\n",
       "2018-02-15   -33.0\n",
       "2018-02-16   -33.0\n",
       "2018-02-20   -33.0\n",
       "2018-02-21   -33.0\n",
       "2018-02-22   -33.0\n",
       "2018-02-23   -33.0\n",
       "Length: 6106, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i['corn'].position()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other key feature of Instrument is the roll progression, which tells us which contract we want to be in at what time. Our rolling system is not smart; it works like clockwork. Using settings from the instruments.py file, we generate a sequence which says which contract we want to be in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:50:37.787825",
     "start_time": "2017-07-22T10:50:37.741733"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1960-12-20    196112\n",
       "1960-12-21    196112\n",
       "1960-12-22    196112\n",
       "1960-12-23    196112\n",
       "1960-12-24    196112\n",
       "1960-12-25    196112\n",
       "1960-12-26    196112\n",
       "1960-12-27    196112\n",
       "1960-12-28    196112\n",
       "1960-12-29    196112\n",
       "1960-12-30    196112\n",
       "1960-12-31    196112\n",
       "1961-01-01    196112\n",
       "1961-01-02    196112\n",
       "1961-01-03    196112\n",
       "1961-01-04    196112\n",
       "1961-01-05    196112\n",
       "1961-01-06    196112\n",
       "1961-01-07    196112\n",
       "1961-01-08    196112\n",
       "1961-01-09    196112\n",
       "1961-01-10    196112\n",
       "1961-01-11    196112\n",
       "1961-01-12    196112\n",
       "1961-01-13    196112\n",
       "1961-01-14    196112\n",
       "1961-01-15    196112\n",
       "1961-01-16    196112\n",
       "1961-01-17    196112\n",
       "1961-01-18    196112\n",
       "               ...  \n",
       "2018-02-14    201812\n",
       "2018-02-15    201812\n",
       "2018-02-16    201812\n",
       "2018-02-17    201812\n",
       "2018-02-18    201812\n",
       "2018-02-19    201812\n",
       "2018-02-20    201812\n",
       "2018-02-21    201812\n",
       "2018-02-22    201812\n",
       "2018-02-23    201812\n",
       "2018-02-24    201812\n",
       "2018-02-25    201812\n",
       "2018-02-26    201812\n",
       "2018-02-27    201812\n",
       "2018-02-28    201812\n",
       "2018-03-01    201812\n",
       "2018-03-02    201812\n",
       "2018-03-03    201812\n",
       "2018-03-04    201812\n",
       "2018-03-05    201812\n",
       "2018-03-06    201812\n",
       "2018-03-07    201812\n",
       "2018-03-08    201812\n",
       "2018-03-09    201812\n",
       "2018-03-10    201812\n",
       "2018-03-11    201812\n",
       "2018-03-12    201812\n",
       "2018-03-13    201812\n",
       "2018-03-14    201812\n",
       "2018-03-15    201812\n",
       "Freq: D, Name: contract, Length: 20905, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i['corn'].roll_progression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Account Curve\n",
    "\n",
    "AccountCurve is an object that takes a list of of positions from the instruments, and calculates the returns based upon owning those positions.\n",
    "\n",
    "How it works:\n",
    "1. Get the positions, either as a parameter, or from a portfolio that's been submitted.\n",
    "1. Multiply the positions by the return for that day (`prices.diff()`) and remove:\n",
    "    * **Commissions** - fixed charges per contract charged by IB\n",
    "    * **Spreads** - the difference between the Bid-Ask price (sometimes called, 'crossing the spread'). When we buy a contract, we pay the ask price, and when we sell it, we pay the bid price. The difference is called the 'spread'. When we buy a contract, it will show an immediate loss, as we value it at the price we can sell it for. Paying the spread is a cost of trading.\n",
    "    * **Slippage** - this is difference between our expected fill price for an order, based on our model, and the actual fill price. For example, we use the settlement price for Corn yesterday to make a trade today. However, we are unlikely to be able to buy corn at the exact price of yesterday's settlement. The difference is called 'slippage'. In our code, there is a factor called `slippage_multiplier`, which is set to `0.5`. This means we expect the price we transact at to be halfway between yesterdays settlement price and today's settlement price. It's a very simple estimate, but it's better than nothing.\n",
    "1. Add all these together:\n",
    "    * **Position returns** - the returns from holding the portfolio that we had yesterday\n",
    "    * **Transaction returns** - the returns from just today's trades, minus expenses\n",
    "    \n",
    "The final result is a DataFrame at `accountCurve.returns()`, which shows how much money we would have made on a daily basis.\n",
    "\n",
    "### Important - fixed capital\n",
    "AccountCurve uses fixed capital. For example, if the capital was £500,000, it will show the daily return on £500,000. It does not reinvest the returns, and it does not accumulate losses. Every day, it imagines £500,000 is invested. In reality, if there were to be a large drawdown, that could bust an account.\n",
    "\n",
    "We do it this way because:\n",
    "* As we are using leverage, we do not need £500,000 to buy £500,000 of contracts. So the money in your account has nothing to do with what you can/cannot buy.\n",
    "* We do not need to 'feed forward' an account balance, we can vectorize the calculation (rather than using a loop), which makes calculating the backtest hundreds of times faster.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:50:39.664753",
     "start_time": "2017-07-22T10:50:37.789835"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7753df24e0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+BnxEREREYUC+Jk1ebTIYwYrtvfzNI7tAqVCgpl6HN22ygUpF4G4f5YVI3vry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UVuZviIiIjIGhjwERWIti4PAsEQ+rx+AMDiGdEedZefO8HoZXnr6gsn46k7zsPNV8wCAPxrw1F8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\nrov9LRNEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f774e62db00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i['corn'].curve().returns().cumsum().plot(title='Cumulative returns for Corn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How the account curve calculation works on a portfolio\n",
    "\n",
    "1. Call `Instrument.positions()` for each instrument in the portfolio, and put together in a DataFrame.\n",
    "1. Calculate the volatility on that portfolio. Calculate the following normalising factor:\n",
    "    $$\\text{Vol Norm Factor} = \\frac{\\text{Daily Volatility Target}}{\\text{Volatility of unnormalised instruments}}$$\n",
    "    \n",
    "    The purpose of this is to make the volatility of the whole portfolio hit our volatiltiy target. Remember the $\\text{Daily volatility target} = \\text{Annual volatility target} \\div \\sqrt{252}$.\n",
    "1. Multiply the `Instrument.positions()` DataFrame by the volatility normalising factor.\n",
    "1. Finally, apply the `chunk_trades` function, which is designed to only change the position when there is a change in volatility of >10% on a particular instrument. We do this to try and reduce trading costs.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:51:18.829876",
     "start_time": "2017-07-22T10:50:39.666942"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7753ac0a20>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tjsHbiw9Do9Ho1Xtn2VH89W/pVNBeHXQDorhkOWRZ+dXSp/qgUKXGncQs4dyG\nU0CpEhgEEhEREdFjk2XlY8/xu8L59qOlo3KW4qptoX8iNgmfrv9Xr/x6fNFmL/N/Po2xCw8ZDAr9\nGjuhX6cmOmVTV0XpbBxjypJkuTrnLlpTQ4nKwyCQiIiIiB7bxgPXjV4rVGkQl5yN2WtPIK+gsMJt\nrtkTa7B80aZziI5JEkbCxi06rFdHLBbhHwPrA2evrb1NampKQoocc388pVMW6OdWR70hU8QgkIiI\niIge27kbqWVe/3T9v0hMy8WGv4wHiwkpcizdfA5ZOQqoDYzuaVu713CAWEJiaYEhff2Ec78mjsLx\nidgkg6OHpkI7AAxo7oLFE4O5GQxVCv9aiIiIiOixJKfnGiwP9HODk72VTll0TJLRdr757SJi76Zj\n97E7uJtY+R09Px3zpHDs6iDF8919sSI8BD9Mfwqzw7oK19bsia2WTWvqg4mDOsDdyaauu0EmhkEg\nERERET2WFdsuCsc925duyNLF3wOZcoWhl+gpVKmRmlm0ccvhc/dx6XaacM3aSoxnn/TBgvHdYS0R\n6722ZRMnfPNBb/h6OWD5pF546/nW8PdxBgA42lkJu4mKtXYVvf0gS68dU+FgKwEALH23J2yl3BCG\nKo9/NURERERUJWMXHtI579HOS8gNCAAhgd5wd7bBkl/P6dQrUKigVKnx/tf/YOKg9niyjScOnknQ\nqbP72B0AwNsD2qJne2+hfPVHfXHsYiJi78ow/OlWcLTTHWl0srfWSZyu7Yvx3THr+xPCeb6iEFIr\n0/s6bCuVQCQSwdVRWtddIRPFkUAiIiIiqpCxCw8JgV9uvv4GL2Nfagu74pEpqZUYIpEIbZu54N1B\n7dGzfSMEB3gBKMr9t2LbBQDA6l2XMXbhIWw5dNPgPQ2N/IUEemPCywF6AWB5vFxs8c0HvYXzd7+K\nrNTr6wtloQpWlvwaT1XHvx4iIiIiKpd2jr2cfCUitl/Uq2MptsCoF9qgfQtXfDa2NDF71zaeeHtA\nOzxMzwMA/HrwRoWnYwa1cn/Mnuuyt5HonN9Lqvzaw7q07chNyLIKdHIyElUWg0AiIiIiKle6vDRp\ne/iKf3CtOFdfCStJ0dfKRq62mPJ6EDyc9Tcr6RFQtF6wvJ1EtYktqv/r6uqP+grHn/2kn4ewvkpO\nz8XvJ+IAAMpCdR33hkwZg0AiIiIiKldZG7w42Vvhq/d6ldtGaGfDa/WM6dWhUfmVquDRKabyPGWN\n3Ke6yXNw7LgLAAAgAElEQVRNo59U/zEIJCIiIqJyrdxxSa/speBm8G/qhLmjnoStVGLgVbpEIpFe\n2UvBzTCifyssmdhTKJs2ohPa+DpjzIttH6/TZVg+qTRoTc8uKKOmvt9P3sPnP/2LfEXFE99XB+0R\nVDvuCkqPgX89RERERFSm7Udv6ZUF+rnpJGOvqD4dvRF5IREA8GrvFhjYq4VwrVtbTzRytUXbZi5o\n28yl6h2uACd7awzs2Rx7o+5iZ+RtBPq5oV+n8kcqM+QF2Ha46PM4djER/bv61Gg/te0/cU84nvVm\nl1q7LzU8DAKJiIiIqEz7okuDj/5dmyIpLRfhQzpUqa3RL7TF6BcMj/C980r7KrVZVd5utgCA8zdT\ncf5mKrq19Sx3RFN7V9RHN5mpLV38PdDY3a5O7k0NA6eDEhEREZFReQW6Ux5H9vfHlNeDamTDltpm\n90gQdydRf6fQrFwFrt5LF86vJ5RuiLNmbyzGLjwEtVpTc50spv0crAykzSCqDNP/r5eIiIiIakxK\nRp5w/OyTtTf1sTYEtHDVOV+25Tw0Gt2Abt66U1j86zkkPJTj79PxBjfIuZecjexcBVK1Pqvq9t7y\n0pyGQ/tVfhoukTZOByUiIiIioxLTcgEALbwdqrQGsD6zEImwbmYoCpQqTFx2FAAwbtFhfDmhBzw8\nHPAwPVcI+hb/es7oLqIXbqZiz/G7AIA10/rBUly94yw7I2/rnLs4WFdr+2R+OBJIREREREZ9vycG\nANDZ3wMSy4b51fHRlBGz1pwAABy/lCSUlZVGoiQABIC7Sdl4kJqDmDuyaumbSq3G3qi75dYjqoyG\n+V8yERERET027amRUquGPYFszbR+Ouc//S8Gd5KyjNb/bGw3TBjYTq9cnqvEJz+cxLIt53H5Ttpj\n9yupeCS2xIyRnR67TSIGgURERERkUJIst/xKDYSl2ALO9lbC+fbDN2FtaXwDFh9Pe/QIaAQvV1ud\n8nX7rwjHX225UKW+qNUa/HbkFu4lZWPOj6eEcm83W7T2rdnUGWQeGAQSERERkUGyrNIk6ik1uOlJ\nfbHsvV4652eup+icd3jCDY3d7TDq+dZCWfIjgfKj00bHLjyEsQsPVaoff/0bj/0n7uGzn/4Vylwc\nrDHGSGoNospiEEhEREREBh06myAcD+zVvO46UktEIhGe6mw8Yfyl22n44u3u6BtUflL5R916kFmh\nenkFhdh6+KZe+aTBHdCyqVOl70tkCINAIiIiIjLo3I1UAMD7QwNhV04S9YYi7NnW+HHGUwavjR+g\nvwbQr7FjhdpVqSqWS/DDlcf0ylwcrOHrZV+h1xNVRMNe4UtEREREj621j3Ndd6FWiUQi7F7yMk5d\nvA8HWwniH8px+loKurXz1Kvbv6sPbu2JQaCfGy7eMr4RzMKNZ/Hms/4I7dy0zHsrlGq9so/f7AKx\nBcduqPowCCQiIiKiMkmtjG+Q0lBZWIjg16Ro+qWniy26tNYPAAGgW1tPWFlaoLWvC/48FYe9UXfh\n6WKDkf1bYcW2izp1N/x1HYFPuMHd2cbofbu29sDpa7prEd2cpI/5boh08ScFIiIiIiqTSCSq6y7U\nWyKRCJ38PWArtcSrfZ7Aqil98OWEHgj0c8e6maHo1lY3eJz+XTTup8gxYclh/Hbkll57KnXFpo0S\nPQ4GgURERESkZ/PBGwCK0hJQxUmtLHWC5gkvB+itGzx49j4KVRrsP3FP7/WKwqLpoK/2eQJiC5He\njqVE1YFBIBEREZGZ0U4Cb8xf/8YDABLTzCdXYE2wEInwcVgX2ElLV2EdOXdfOB678BDCV0QK57Ks\nfIgADAhuhrXTn4KLg3VtdpfMBINAIiIiIjOi1mgwbtHhMnPXKZSqWuxRwycSiRAxuQ8mDNTfXRQA\ncvILkfBQjglLjiAxLRfWVmJOwaUaxY1hiIiIiMzIo8nNDdnw13XhuIu/R012x6yUNao3d90p4Thf\nwSCcahZHAomIiIjMyOy1J4XjsQsPQW1gauixS4nC8XuDO9RKv8xBa1+XCtXr3s6rhntC5o5BIBER\nEZGZMBTwvb3osM4aNW1Pl5PTjirvmw96Y/QLbcqsMzy0ZS31hswVg0AiIiIiM3H5tuFk5v/98xom\nLDmMvIJCqNSlycpfYzBS7extJOjTsXGZdZzsuRkM1SwGgURERERm4lp8htFrhSoNEtNyIc9VCmUS\nS35VrCkrJ/fWS78RHNAIk4cF1lGPyJzwv2wiIiIiM/H7iTjh+P8m9NC7XqhSI+auDADQI4Dr0mqS\nrVSCBeNLn0ELbweMH9gOgX7uddgrMhcMAomIiIjMwLW4dOH4/SGBaORqi6Xv9tSps3DjWfzwvysA\ngBMxybXaP3MVHNAIAPBij+Z12xEyK0wRQURERGQGjl54IBwHtSoabXJ1lCI4oBGiY5L06k95vWOt\n9c2cvT2gLV4OaQ4vF9vyKxNVE44EEhERETUAizedxYlY/WCuxKVbhjeFGdG/FeaM6qpT9kIPX7Rv\n4Vat/SPDRCIRA0CqdRwJJCIiIjJxMXdluBqXgatxGejRrpHe9dsPspCTXwgA+PA13RE+exsJ7G0k\nOmWN3exqrrNEVOc4EkhERERk4jQG8v9p++vf0g1hHG2tDNZZML57aR07w3WIqGHgSCARERGRiTt4\nOqHM66euPBSOfb3sDdbxdrPDsvd6IeaODO1buFZr/4iofmEQSERERGTClIUqXDCy3g8AktNzheNG\nrrYQiURG67o4WCMk0Lta+0dE9U+FpoNeuHABYWFhAIDY2Fj07t0bYWFhCAsLw/79+wEAW7duxeDB\ng/Haa6/h8OHDAID8/HyEh4dj5MiRGD9+PGSyorwz58+fx7BhwzB8+HCsXLlSuM/KlSsxdOhQDB8+\nHBcvXgQAyGQyjB07FiNHjsTkyZORl5dXfe+eiIiIBI9OKVy44QzCV0TWUW8ahm1HbmLswkNIluXq\nlGflKLB2byziH8of+x6//n1D51yt0UCt0SA9uwAAcP5GqnDNUG5AIjI/5Y4Erl27Fnv27IGNjQ0A\nICYmBmPGjMHYsWOFOikpKfjll1+wfft2FBQUYOTIkejVqxd+/fVX+Pv7Izw8HPv27cOqVavwySef\nYN68eYiIiICPjw8mTJiA2NhYaDQanDp1Ctu2bUNiYiLCw8Oxfft2rFq1CgMGDMDgwYOxZs0abNmy\nBaNHj66xD4SIiMgc/fVvPDYfvIEvJ/SAl6stlIUqXE/IBACo1RpYWBgfPSLDbj/IEpKzz1pzAm2b\nu0KWmYcXejTDhZupOHcjFdExSfhxxlNljs5pU6s1OHrhAbq39cTBMwno1cEbrZo648j50vQPxy8m\nYv3vVwEUBX0HzxRNFQ3gFE8iKlbuSKCvry8iIiKE88uXL+PIkSN444038PHHH0Mul+PixYvo1KkT\nrKys4ODgAF9fX1y9ehVnzpxB7969AQB9+vRBdHQ05HI5FAoFfH19IRKJEBISgqioKJw5cwYhISEQ\niURo3LgxVCoVZDKZXhtRUVE19FEQERGZr80Hi0aTzt5IAQDEaY1QJabl1EmfTN0X/z2tc37lrgzJ\n6Xn46ferOKc1OpdXoKpwm28vPoxf/ryGSSv+wc5/7mDNnhi9HH8lASAA3E/JgcSy6Ovey72aV+Fd\nEFFDVG4Q+Nxzz8HSsnTAMDAwENOnT8fGjRvh4+ODb7/9FnK5HA4ODkIdOzs7yOVynXI7OztkZ2dD\nLpfD3t5ep2555Y+2QUREVF+Vt0tjfZRbnDpAW2pGvnCclpWvd52MG7vwEMYuPCSci8sZRZ20IhLK\nwooHgtquJ2Ti8p2i5TY92nnpXT91JRmJaUVTUd0cpVW6BxE1PJXeGOaZZ56Bo6OjcDx//nx07doV\nOTmlvxLm5OTAwcEB9vb2QnlOTg4cHR11yrTLJRJJmW1IpVKhbkW4uNjC0lJc4ffl4eFQfiWqN/i8\nTAufl2nh86q63HwlJi46hOd7NMOI59oAKAoKl244g9bNXPByH78qtRufnA1LsQW83fVztz3O81Kr\nNfjf8dtYu+uyULbt8C28GOKH7/fECGVSG2v+XVTQOwv/1isb/0p7fLfzUpmv2/7PXYS/FlRu+829\nHXE3McvgtRmju+HV6Xt1yv69WroraGs/j3LbJ138uzctfF4VV+kgcNy4cZgzZw4CAwMRHR2NgIAA\nBAYGYsWKFSgoKIBCocCtW7fg7++Pzp074+jRowgMDERkZCS6dOkCe3t7SCQSxMXFwcfHB8eOHcOk\nSZMgFouxZMkSjBs3DklJSVCr1XB1dRXaGDx4sNBGRaSn55ZfqZiHhwNSUjjCaCr4vEwLn5dp4fN6\nPH+cjIMsKx+b/rqG/p2bAADuJWUj8vx9RJ6/j+C2npVus1ClxrtLjgCAsF6vRGWel0qtxvjFR2An\ntUTE5D5Izy7AR98eN1h3zPy/dM6TU7P5d1EBhSo17qfoTp1d9E4wPJxt8F3xedc2njhdHJj5N3US\n1l0mJGdh4Ee7MbBnc7za5wmj94hPNv4c0mU56N+lKf4+k4DgAC9ExyTrXOczrBz+e2ha+LwMMxYY\nVzoI/PTTTzF//nxIJBK4u7tj/vz5sLe3R1hYGEaOHAmNRoMPP/wQ1tbWGDFiBGbMmIERI0ZAIpFg\n2bJlAIDPPvsMU6dOhUqlQkhICDp27AgA6Nq1K15//XWo1WrMnTsXADBx4kTMmDEDW7duhYuLi9AG\nERFRfZMhLxCONRoNRCIRjp6/L5Qdv5SIXh0qt/3+7Qeloz4rd1zC/dSiIKNraw/Mm9Czwu2kZRZN\n6cwpnvp56GzZeeW0KZTqCtc1Z9rPqmUTJwzu8wQ8nIs21nuqcxMcPnsfvQO9MSC4GWyllnBzlGLc\noqId1WPvpgMA9kbdRceW7jhy/j5GPN0KNtalX9VkWflQqcuebjy8fysM6t0CtlIJQrs0xYL/ngGA\nMgNLIjI/Io0pLl6ogMr8EsBfDkwLn5dp4fMyLXxej+ezn/7FvaSiz6+Lvwc6+bvjh/9d0anz+dhu\naOppOFm3Sq3G36cT0KdjY+HLf+xdGZZuPm/0nutmhlaob5sOXMffxbtEzhjZCduO3NIJWsoyrJ8f\nXujRrEJ1zVWSLBf7T9zDsYuJaNnUCR+/qTtzqVClhgIi2Ip11wcmpMgx98dTOmVOdlbIzFFgSN8n\n8PuJOOQWFOL7qf3wn6VHAAAtvB0xbUQQ1Oqi9YQAEBzghfEDA/T69d7ySOQVFFZqB1Iqwn8PTQuf\nl2HVNhJIREREhpUEgABw5noKzlxP0a+TnG00CNwffQ87/7mD01cfYvZbXQEABcqqbRjyqJIAEAAW\nbTqnc61/16YY2d8fa/fGCFMI2/g64+VeLbD413Pl9uHUlWRYWYoRl5yNp7s2hZ1UUi19ro/UGg3i\nk+Xw9bLXCao+XnNCOO5gIBWDpdgC3ga+pDb10P9byMxRAAC2H70tlH2rtabQy8UGUivdr3CtfV0M\n9ndFeAhEIjAAJCIdDAKJiIiqwTkDAZ8hlmLjG3OnZRVNJ03JLN2Ns0BRdgCWLMuFBoC9jQSrd11G\namYevni7OyRam6Ot0tr4xZDnnvQFAIwfGICBvVrA0dYKtlJL3CnegKSs6aDX4zPw3e7STWR2HbtT\n4dHJ+kZZqMLpqyno7O8BayvDm8v9fToBmw/ewEvBzTCkr+GNfrq11d+lsyzrZoZiwS+nceu+8ZHZ\ni7fShOOBWqkePJylSMnIR9fWhjd9KUkPQUSkjUEgERHRY1IoVYjYYXz3x+CARlBrNDgZmwx5nhK5\n+YWwler/L/jCreLcccUrNQpVaqzZG1vmvWdpjUCVePerSAzs2Ry7jt3Bqil9hI1IDHnjGX+4OZWm\nDmiktfGMVXEA8cepOHi62EAsFuH2gyy89VxrYWRp4cazZfbPlOyIvI0/T8UDgNHpkyeKc/Lti74n\nBIGPTqvV3rynomaHdcWs76ORnJ5XZr2Vk/vo/O3MG90N8jwFbBvw6CsRVT/+PERERFQF1+LScfxS\nIoDSROvGvBbaEl38i0ZqNh64jkkrIhF7V6ZT58j5+8iUF00DzM5VAgCuxqUL19s1L5ru179LU4x4\nuhWCWrobvZ9KrcGuY3cAAFfuputcc3O01jl/uktTo+042lkJx//98xrW77+Ko+cfIPLCA6OvMWUl\nASAAHDp732Cdu1pTfks2ApJVUx7FaSM6AQDmj+uGdTNDsW5mKMa82EanzqM/HthKLeHpUvmgk4jM\nG0cCiYiIqqBkXd2P+3Q3flk+qRc+XFmaeqFPR2842VnpjSot3Xweo55vjatxGTgZm4xWTZ2EayUj\ncyVBIQBMHd5J2HEUAC7dTkNFaI9QTh/RCfa2EmEjkv5lBIAA4GBrZbA8NbPsoCdDXgBne+sy69R3\nigqsxTx/IxX9OjUpc4pvZbg6SvWm0rqY+OdIRPUTRwKJiIgqKUlmOBftB0MD4aT1pX3B+O4Y/UJb\nAICzvX5A9fMf13AytmgjlhvF+eKA0iCrJMB879UOAHQ397hxv7R+RbVp5gKJVsBSkbQB415qq1dm\nUdyP3HylUPbjjKeE4x1aG5qYkqYedsJxyeYsJXLzCzF24SGdMgdb/SmYbXydq7VP7Z9ww0fDg/B0\nl6YIH9yhWtsmIvPFkUAiIqJK+tjAOrwJL7dDx+Ipmqun9IVYLNIZIfJr4qT3mrJo7zTasqn+a6e8\n1hFfbjiLaSM6QaFU4evfLpbZXqdWRX1z0gpGpUY2P9HWq4M3glq54/yNVMiy8rHznzu4XbxhjPZa\nOO0ANeaRqa6mws1RioTiZO95BYU61/4+E69XP79405707KKgvbO/ByYO0k/T8LgCmrsioLn+jqNE\nRFXFIJCIiKgSHg0OAKBHgBd6tGsknBvbWXL2W12QmpGPbm09cTsxS0jkra2xux0epObgs5/+Fcqc\n7PRHEVs1dcbeZa8IKQdKphE+Olrl62WPuGQ52hUHEVIrS3z7YR9oNBVPG2AnlaBXB28huIu5I0Nc\ncrYQBJZMZR3Wzw/bjtxCenZBhdqtb7SDditL3WeonSajtY8zrsVnCEFgSSqHQD83iC04yYqI6j8G\ngURERMUKlCp8u/MSXurRzGjeteR03amgnVq5Y4KBJN2G+DV2gl9jJ+H4tadaQmJpgY0Hrgt1mjdy\nwIPUHOG8d6B3pd5DSTCYmJYDFwdriC0scDUuHe21cteVJKKvLO2Rw0/Xlwapw/q1BAA886QPth25\nVaW26wNFYWkqjHxlabBfoFThelwGgKKdP18MboZr8RnYeOA6rsWlI7f4h4GOZWzWQ0RUnzAIJCIi\nKrZi6wVci8/A5dsyo7nucvJLg4MF47vD283OYL2KeL57UX6+Rm62WLb5PNo2c9HZfRIAhvQznIuu\nPNr96vCEW5X7qO0Jb0eD5SUb2WiPpN1JzEKzRg7C+kFToCwsHe07fikJaZn5+Gh4EOb/fFoIzD8Y\nGoicvNK1kKevleaHtLfh1yoiMg3814qIiAhAdq4C1+IzhPN8RSGkVvr/m5QVb9oysn+rxwoAtQU0\nd8XiicFwsrNCXLIcC34pnSbqaGSHzrogEonQqZU7zt1I1Sl3MrDpzfyfT8NCJMIPWhvG1HfaI4EA\ncDUuA+MXH9Epc7S1gpeLjcHXcyooEZkK/mtFRERmT6PR4INvjumU7frnjsG663+/CqDqUyqNcXey\ngcRSXOkNZGpb+JBArJsZqrOTpvZo36djnhSO1RoNNMWJ702BQqku87naWFvCVmppcC3lm8/612TX\niIiqFYNAIiIye9pT+kr89a/+bpDajOXQqw7zRj+JFt6OmFSPUwL4eNobLPf1ctA5f3R6a32mLFTB\nytICa6f3w4r3Q/Suf/thH+H4+6l90a550bpR/6ZOCO1cds5FIqL6hEEgERGZvfiHcuHYSlL0v8Yn\nGhte/1ailYG0DdWlWSMHzBnVFZ39PWrsHo8r7LnWaOJuhzmjuupd+2BooHA8/+fTuFzBxPZ1TalS\nQ2JpAbGFBRxtrfDF292Fa6un9NWpK7EUY8prQRj1fGt8MKxjbXeViOixMAgkIiKzoixUY/rqKERd\nThTK5LlFicGbeNhhVfGX/dsPsnDwTILRdkxpmmNNkFpZYv7b3dHCwGYxHVu6Y9TzrYXzktx79Z0s\nqwBZWkniG7vb4dsP++C7j/oaTPthYSFC36Am1T41mIiopjEIJCIis3LpdhpSM/Pxw/+uCGV5xfne\nJg/tqLO+beOB6zo7RmoztGkMleob1ERIUK9UqcupXfeyi38IeHRzGBtrS1hJDOd9JCIyVQwCiYjI\nbKg1GqzccUk4z5QXYNyiQzgZmwwAcHGwBgCMe6mtUCclI1+nDUdbCQJauMLCwnRSH9SVV0JaAAAO\nlLO+sj7IK871F8Rcf0RkBhgEEhGRWbKTWuLWgyxoz+osCex6dfBGSYj3yQ8nodaqNOONznhbK0gk\n47xcbQEAcq28evWVsngEsOSHACKihoxBIBERmQ3tqZ4aDfAwPU847xfUWKdumNaatpJ68jwlTl99\nCBFHASvEWmsaZcxdWb1eRxl7Lx0AcOFWajk1iYhMH4NAIiIyK8EBXgCA3IJCbD18EwAweVgg3nq+\njU69fkFN8HTxtv/z1p1CgUKFqEuJ2PnPHWw/cqt2O23CmhWnjFi2+Xy9nhb66983ABRtDkNE1NAx\nCCQiIrPSr1MTBLRw1Slr/4Sbwbp3krIAFE0VnPjVUWEE8OkuzAlXUfdTS3cGvRafUYc9MU57uu9H\nrwfVYU+IiGoHg0AiIjIb52+k4ssNZ5GiNQ0U0J0mqm3cI2v/SkaLjCVKJ31L3+0pHJ+7kVovp4Rq\n/z20aeZchz0hIqodDAKJiMhsfLP9IgBAO+ZbO72f0frebnZYNzMU3m62OuUiI0Ej6XO0s8L7Wsnj\nM+QKZOUocOpKMgoUhtNv1Kavtp7HrDUnhHOxBb8aEVHDxyRHRERkFi7eShOOG7vbIbl49KciX/od\nba2QmJZbY31r6Dr6lU63/ejb4/BwlgqpN9bNDK2rbqFQpcbl27I6uz8RUV3hz11ERGQWVmy7IBx3\na1u0OUyfjt4Vem1KZul0wRkjO1Vvx8yASCTCtOGla+20cy/W5fTQuGS5zvnU4VwPSETmgUEgERGZ\nna5tPNC/S1P069SkQvWH9PETjv2aONVUtxq0ts1dDZav238FkRce1HJvihSq1DrnbZq51Ek/iIhq\nG6eDEhFRg3bhZqrOGr55o5+E2MICI5/xr3Abwe0bIbh9o5ronllp5GqLJJnutNrjl5Jw/FIS+nRs\nbORVNWfhxrMAgJZNnfDxm11q/f5ERHWFI4FERNRgnbqSjK9/u6gzFdTD2aYOe2Te/vNygNFrF2+l\n1dnUUGO7wxIRNVQcCSQiogYr6ZHNXAaFtICtlP/rqyvNGjlg+NOtsPngDb1rK7ZdgJerLR6m56Jd\nc9dayddnbyOBPE+Jj17vWOP3IiKqTzgSSEREJi81Mw+yrHy9kaRdx+7onPet4BpAqjnPPumDdTND\n8UJ3X71rybJcaDRAzB0ZdkTervGRQTcnKawlYkgsxTV6HyKi+oZBIBERmYxClRqf/HASv5+4J5Rp\nNBpMXx2NqauisPXwzTJf72RnVdNdpAoa0LM5AOC5bj4IH9xB7/r/ou4iJSNPr7w6FRaqIbHkVyEi\nMj/8l4+IiEyGQqnCg9QcRF54gP/+cRWZOQpsPlga+P15Kt7oa22tOQ20PrGxtsS6maF4PbQVOvl7\nGKwz8/sTBsuri5JBIBGZKf4fkYiITEZCSg4AIDk9D8npeYiOSUaBUiVcd3OUQq3WACIgpTgZvKuj\nNeykEowf2K5O+kz1U2JaDh7W8EgjEVF9xSCQiIhMRvxD3eTe2gEgAKRl5WPd/iuIS86GvY0EAJAp\nV2Dpu71qrY9UNW6OUqRl5euVj114COGDOxgdLawMZaEaF26m4p+LiXB1tH7s9oiITBWDQCIiMhk3\n72eWWyfqcpLOefgQ/fVmVP8snhiMGd9FIzVTPxCM2HEJ62aGPlb7ao0G/1l6RK+85McCIiJzwonw\nRERkEtRqDU7GJhu8NuW1jmj/hKvBa619XWqyW1RNRCIRFk/sKQTt1R2cKZVqg+VfTeIoMRGZH44E\nEhGRSchTFOqcfz62G1Iz8xHY0g0WIhEiLybqvcbW2hLWEm7/b0o6tfJAxOTesLIU64zc5RUUwtpK\nXOXE7gWFKoPllmL+Hk5E5of/8hERkUkoVBXljAtq6Y7pIzqhqac9glq5C0FBgUL/S74GNZtnjmqG\nnVQCiaUF1s0MRVBLdwDAe8sjseVg2SlAypKTp9Qr++Lt7lVuj4jIlHEkkIiITMLWQzcAAH2CGqNN\nM/0pno62RdMHQzp4Y+xLbRF1ORHtmhueIkqmw0YrtceB0/EY0b9Vldo5pjVS/HFYF7Rs4vTYfSMi\nMlUcCSQionqvUKVGdEzRekCN2vDo3uC+fmjX3AV9gxoDAHq294azPXeANHUSS93pnzF3ZFVq5/eT\ncQCAfp2aMAAkIrPHIJCIiOq9fK2pnkGt3A3WcXGwxtThneDHL/gNSsyddJ3zZVvO66QG2X70FsYu\nPISLt1KhUBpe96etrYFRZCIic8MgkIiI6r0HqUVJ4v19nCGq4sYgZJqmjggCAFhJSr+yTFx2FD/+\nLxZZOQrsi74HAFix7SLeWXZUJxDUaDRY8us5bDtSupYwqKVbLfWciKj+4ppAIiKq924/yAIAKI3s\n8EgNl5eLrZAjcOzCQ0L58ctJOP5ITkgAuB6fgfZPFAV6uQWFuHIvHVfuFY0mBjR3gcSSu8USEXEk\nkIiITMaAns3rugtUh7zdbMutU7KLLAAUFurmBoy5m/5odSIis8QgkIiI6r2th4um80mtOIHFnE0f\n2bncOkmyXOFY+UgQ2MTdrtr7RERkihgEEhFRvfYwI084trHmVD5z5mRnhc/GdiuzTskPBvdT5Jj+\nXbuLoMYAACAASURBVLTOtbeeb11jfSMiMiUMAomIqF6bqfVFnikfyMfTHgvfCdYp+35qPyz8Tw+d\nsjk/njL4WiIiYhBIRET1mPZ0Ph9PewaBBADwdLYRAsE+Hb0hsbSAp0vpekHt0eMSQS3dOZ2YiKgY\n/zUkIqI6l5OvhKXYAtaS0umeK7ZdwMVbacL5oN4t6qJrVE95OtsIu4aWaNvMBVfupWPdvitC2UvB\nzdCrgzfcnaS13UUionqLQSAREdWo6/EZWLjxLCYOao8n23jqXMuQF2D+z6eRnl0AAMKX+ti7Mp0A\nEAA6tfKonQ6TydJoinYGvR6fAQDwcrHBkL5+ddklIqJ6idNBiYioRi3ceBYAsHrXZeTkK3WuLdp0\nTggAAQiJvndG3tapV95mIEQAcDUuQ+f80zH8uyEiMqRCQeCFCxcQFhYGALhy5QpGjhyJsLAwjBs3\nDqmpqQCAL774AoMHD0ZYWBjCwsKQnZ2N/Px8hIeHY+TIkRg/fjxkMhkA4Pz58xg2bBiGDx+OlStX\nCvdZuXIlhg4diuHDh+PixYsAAJlMhrFjx2LkyJGYPHky8vL05/kTEVH9cz81B3cSs3TK5Lm6QWCy\n1nb+AJCvKAoCvVx188E19eDW/lS+2WFddM6trbibLBGRIeVOB127di327NkDGxsbAMCCBQswZ84c\ntG3bFps3b8batWsxa9YsxMTE4IcffoCrq6vw2vXr18Pf3x/h4eHYt28fVq1ahU8++QTz5s1DREQE\nfHx8MGHCBMTGxkKj0eDUqVPYtm0bEhMTER4eju3bt2PVqlUYMGAABg8ejDVr1mDLli0YPXp0jX0g\nRERUPb7fHYOEFLlOWUHxSJ8x1+IzcO56Ck7EJuuUi0Siau8fNTx+TZyEY0c7qzrsCRFR/VbuSKCv\nry8iIiKE86+++gpt27YFAKhUKlhbW0OtVuPevXuYO3cuhg8fjt9++w0AcObMGfTu3RsA0KdPH0RH\nR0Mul0OhUMDX1xcikQghISGIiorCmTNnEBISApFIhMaNG0OlUkEmk+m1ERUVVe0fAhERVb9HA0AA\nyJArAAD7ou9i7MJDetfjH8r1AkAXB+4IShX3n5cDAACz3ig/sTwRkbkqdyTwueeeQ0JCgnDu6Vm0\nqP/s2bPYsGEDNm7ciNzcXLz55psYM2YMVCoV3nrrLbRv3x5yuRwODg4AADs7O2RnZ0Mul8PevjRP\nj52dHeLj42FtbQ1nZ2ed8pL6j7ZBRESmJTjAC9ExyVix7QLeHdQe24/eNlgvK6dA5/zLCT04okOV\n0r2dF7q386rrbhAR1WtV2h10//79WL16NdasWQNXV1ch8CuZMtqjRw9cvXoV9vb2yMnJAQDk5OTA\n0dFRp0y7XCKR6JU7ODgI9aVSqVC3IlxcbGFpWfG1AB4eDhWuS3WPz8u08HmZlup4XspC3WmfbVq4\nIzqmaIRv1a7/Z+9OA6Oq7j6O/yaTmSRkEpJA2BP2IIJhlUUCii3FpW64IPjYVnCjgsWFQqmI1NJq\nEaWKWGvlsUUFUaqt9VGrKEQWRYGI7Ivsa0gCyWSZySzPi8CFIYkkIcnkznw/b7z33Hsn/8kxQ345\n956zMeDY+7Nv0Hc7j2vqSytV6gs4pO5d+GX+fPj5Mhf6y1zoL3Ohv6qu2iHwX//6l9566y0tWLDA\nGLnbs2ePJk6cqPfee08+n0/r1q3TTTfdpNzcXC1fvlzp6enKzMxUnz595HA4ZLPZtG/fPqWkpGjF\nihUaP368rFarZs2apbFjx+rIkSPy+XxKSkpS7969tXz5co0YMcJ4jarIyys6/0mnJCfHKTubEUaz\noL/Mhf4yl9rqr/wit7Hds1NT9evSVP/7n/LnPXX/QGVnF8hZUCJJyjlrke/Le7bi/53z4OfLXOgv\nc6G/zIX+qlhlwbhaIdDr9WrmzJlq2bKlJkyYIEm69NJL9eCDD+qGG27QbbfdJpvNphtuuEGdO3dW\nmzZtNHnyZI0aNUo2m02zZ8+WJM2YMUOPPvqovF6vMjIy1KNHD0lS3759NXLkSPl8Pj3++OOSpHHj\nxmny5MlavHixEhMTjdcAADRcpaeG9PqkJev+G7vJGhGhv066QvfOWmac06tzUzVLKLuDxGotm/jl\n9BISV/VL1c1XdKjfogEACBMW/+mVVUNMdf4SwF8OzIX+Mhf6y1xqq7+O5BZp6l+/1JAerfSLqy8y\n2s+eDOaFiYMVG22TVDYhzPT5a4xjo37cWcP6plxwHaGOny9zob/Mhf4yF/qrYpWNBLJYPACg1p1e\n9N0WGfjPTKumZev9vfLrK4wAKEnWiMAlIBwxNgEAgLpRo4lhAAD4IcUujyTJfk4I/P3d/Ss8P65R\nYOgbwOyOAADUGUYCAQC17p+ZZUtAFJZ4qnR+XKMzy0A8fFsPFocHAKAOMRIIAKh1Ow6clCS1PnX7\nZ1U8N36QIiIsAYEQAADUPkIgAKBWvb9yt7H9475tqnxdY0dUXZQDAADOwe2gAIBateH7HElSWkoC\nt3UCANAAEQIBALUq/tTtnONHXBLkSgAAQEUIgQCAWrV5b54kKdpuDXIlAACgIoRAAECt2X/MKZe7\nbI3ASCv/xAAA0BAxMQwAoFYsXXtAb3yyXZIUwbOAAAA0WPyZFgBQK04HQEm65YqOQawEAAD8EEIg\nAKDWdW7TONglAACASnA7KADggh0/WWxsv/Tw5YpiUhgAABosRgIBADV2stAtj9env72/2WgjAAIA\n0LAxEggAqJGiklI99MKKYJcBAACqiZFAAECNfPrNgXJtD9zUPQiVAACA6iAEAgBq5L0VuwP2f9yn\njfp0aRakagAAQFURAgEAtSKvwBXsEgAAQBUQAgEA1eYq9UqSmjaONtpuGNw+WOUAAIBqIAQCAKot\nymbV2Gu76oGbLjHaWjeNDWJFAACgqgiBAIAf5Pf7lbXzuHYePGm0Lcs6qFc/2KKCYrfRZrFYglEe\nAACoJpaIAAD8oNf/u12frz8oqWwheJstQv/4aJsk6cCxwmCWBgAAaoAQCAColLvUawRASXp/1R5t\n2pNr7A/p0UpJ8VFyFpcGozwAAFADhEAAQKWO5hUH7P/fl3sD9htFR6pf1+b1WRIAALhAPBMIAKhU\n9oniSo89N35QPVYCAABqCyEQAFCpD88Z+TvtibsuVWNHVD1XAwAAagMhEABQqRPOstk/h/ZqrSfu\nutRoT2nmCFZJAADgAhECAQCVat8yTlLZBDDNkxpJkrp3SGI5CAAATIyJYQAAlTod9hIcdkXZrJo/\n5cogVwQAAC4UI4EAgEqVenySJFukNciVAACA2kIIBABUyu3xSpLsNv65AAAgVPCvOgCgQtv3n9Dm\nPXmSJGsEzwACABAqeCYQABDA7/fr4zX7tfjznUYbE8EAABA6CIEAAEll4e/bHdmyeL0BARAAAIQW\nQiAAQJL0n1V79O4Xu9X3omYB7dN/cWklVwAAADMiBAIAJEnvfrFbkvTN1mNG25/uH6imCTHBKgkA\nANQBJoYBAFSoe4ckAiAAACGIEAgAqNBDt/YIdgkAAKAOEAIBAMovdAfs//yqLswICgBAiOKZQAAI\nc6Uenya+sMLYn3lPf7VsEhvEigAAQF1iJBAAwtzOAycC9gmAAACENkIgAIS5whJPsEsAAAD1iBAI\nAGHOVeo1tv/222FBrAQAANQHQiAAhLn3V+2RJI29tquaJzUKbjEAAKDOEQIBIMwdyyuWJHVoFR/k\nSgAAQH1gdlAACHNJ8VFyub1MCAMAQJggBAJAmMvNd8kWyY0hAACEC/7VB4AwtizroCQpymYNciUA\nAKC+EAIBIIx99OU+STwPCABAOCEEAkAY69m5qSRpSI9WQa4EAADUF0IgAISx/369X5LUJD46yJUA\nAID6UqUQ+O233+rOO++UJO3du1ejRo3S6NGjNX36dPl8PknS4sWLNWLECN122236/PPPJUklJSWa\nMGGCRo8erXvuuUe5ubmSpKysLN166626/fbbNXfuXOPrzJ07V7fccotuv/12bdiwQZKUm5urMWPG\naPTo0Zo4caKKi4tr790DQBgr9fiM7VZNWR8QAIBwcd4Q+Morr+ixxx6Ty+WSJP3xj3/UxIkT9eab\nb8rv92vp0qXKzs7WggULtGjRIr366qt69tln5Xa7tXDhQqWlpenNN9/UjTfeqHnz5kmSpk+frtmz\nZ2vhwoX69ttvtXnzZm3atElr1qzR22+/rWeffVYzZsyQJM2bN08//elP9eabb+riiy/WW2+9VYff\nDgAIH87iUklSv67NZItkYhgAAMLFeUNgamqqXnjhBWN/06ZN6tevnyRpyJAhWrVqlTZs2KBevXrJ\nbrcrLi5Oqamp2rp1q9auXavBgwcb565evVpOp1Nut1upqamyWCzKyMjQqlWrtHbtWmVkZMhisahV\nq1byer3Kzc0t9xqrVq2qi+8DAISdnPwSSVKCIyrIlQAAgPp03hA4fPhwRUaeWU7Q7/fLYrFIkmJj\nY1VQUCCn06m4uDjjnNjYWDmdzoD2s891OBwB556v/dzXAABcuB0HTkiS0lISglwJAACoT9VeLD4i\n4kxuLCwsVHx8vBwOhwoLCwPa4+LiAtp/6Nz4+HjZbLYffI3o6Gjj3KpITGykyGrc3pScHHf+k9Bg\n0F/mQn81TE6XV5LUpUPTgD6iv8yF/jIX+stc6C9zob+qrtoh8OKLL9ZXX32l/v37KzMzUwMGDFB6\nerrmzJkjl8slt9utXbt2KS0tTb1799by5cuVnp6uzMxM9enTRw6HQzabTfv27VNKSopWrFih8ePH\ny2q1atasWRo7dqyOHDkin8+npKQk4zVGjBhhvEZV5OUVVfk9JSfHKTubEUazoL/Mhf5quLJzyz4n\noyx+o4/oL3Ohv8yF/jIX+stc6K+KVRaMqx0CJ0+erGnTpunZZ59Vhw4dNHz4cFmtVt15550aPXq0\n/H6/HnroIUVFRWnUqFGaPHmyRo0aJZvNptmzZ0uSZsyYoUcffVRer1cZGRnq0aOHJKlv374aOXKk\nfD6fHn/8cUnSuHHjNHnyZC1evFiJiYnGawAAqievwKX8QreSE6L19rJd2rDzuJonxijSympBAACE\nE4vf7/cHu4i6UJ2/BPCXA3Ohv8yF/mo4fvvKlzqcE3iXRJtkh343tp+xT3+ZC/1lLvSXudBf5kJ/\nVayykUD+/AsAYcDn95cLgJJ0INsZhGoAAEAwEQIBIAx8+OXeYJcAAAAaCEIgAISBJcu/r7DdHsk/\nAwAAhBv+9QeAMNSnS7Ki7VY9MOKSYJcCAADqWbVnBwUAmMvXW4+Va7NYLJr38OVBqAYAAAQbI4EA\nEOJeem9jubahPVsFoRIAANAQEAIBIEwM7NZCkmSxSF3bJQW5GgAAECzcDgoAIeyTr/dLkvp1baax\nP+2qGwe3V2y0LchVAQCAYCIEAkAIW7h0hyRp0+5cRVgsSk6ICXJFAAAg2LgdFABCVInbY2wPuqRl\nECsBAAANCSEQAELUhl05xvYtV3QMYiUAAKAhIQQCQIjKyS8xtiOtfNwDAIAy/FYAACEq0RElSfrZ\n8C5BrgQAADQkhEAACFGlHp8kyRbJRz0AADiD3wwAIFRZyv4TE8VE0AAA4Ax+MwCAEOAq9coiyW6z\nGm19uzSTx+vXJR2aBK8wAADQ4BACASAEjJu9XJHWCP110hWSpG378vT0m+vVKCpSl3VvEdziAABA\ng8LtoAAQIjxenzbtyZXP79fTb66XJBW5PPL5/EGuDAAANCSEQAAwse8P5WvMU58Z+7MXZWnngZMB\n50TbredeBgAAwhghEABMxOP1yeP1Gfvrd2SXO+fbnceN7ZRmDlkslnqpDQAAmAPPBAKASXi8Pt07\na5kk6c7hXTS0V+sKA96HX+2TJD15d3+1atKoPksEAAAmQAgEAJN46o11xvaCj7fpv2v2qVv7pErP\nb9mkEaOAAACgHG4HBQCT+P5QfsD+0bxifbbuoCTp+kHtyp0fQQAEAAAVIAQCgEmktWlc6bEbB3cI\n2H/09p51XQ4AADApQiAAmITb45M9MkK3XtGxwuO9Ojc1ti9qm1hfZQEAAJPhmUAAMAmP16dIa4Su\nHtBWHVs3Np4RHNituSRpxOUdVVji0dhru3IrKAAAqBQhEABMIK/ApQPZhcZ+88QYY3vstRdLklo3\njdWUO3rXe20AAMBcCIEAYAKZ3x4K2G/siNKLDw2R3RahiAhG/QAAQNURAgHABJqdGvnr2enMc38x\nUXyEAwCA6mNiGAAwgbgYmyQpI71lkCsBAABmRwgEABM4dLzsecC0lIQgVwIAAMyOEAgADdThnEId\nO1EsSdp71ClJKnF5glkSAAAIAYRAAGiAXG6vfvvKV5ryl9WSpOJT4a9RNM8BAgCAC8NvEwDQAO0+\nnG9sz1zwjawWiyySopkMBgAAXCBGAgGgATpZ6Da2dx3MV5HLo5ioSBaBBwAAF4wQCAANUH6R+5wW\nixLjooJSCwAACC3cVwQADVDSOYGvfcs4dW2XGKRqAABAKGEkEADqkd/vP+85RSUevfjuxoC2LzYc\n1rZ9J+qqLAAAEEYIgQBQD3w+v8Y89ZnGPv15QBDce6Qg4NbPLXvzNH5OZoWvkejgdlAAAHDhCIEA\nUA8O5xYZ29knSySVTf4y47WvNfH5FfL5yoLhrIXrA67r0Cre2I532OuhUgAAEOoIgQBQD/72n83G\n9unn/ZxnjQBu2pNb7hprhEW3XtHR2M+4pGUdVggAAMIFIRAA6sHZSzts3J2rYpdH015dY7Q9t/hb\nSVJMlNVoe+CmS9QlNVH3XHexptzRW5FWPrIBAMCFY3ZQAKgHcY1sxnbOyRLN/25LuXM+X3dApR6f\nOrSK169uSVdco7LbPwd2a1FvdQIAgNDHn5UBoB5s2JVjbBeVlGrttuxy5+w+XCCP169G0ZFGAAQA\nAKhthEAAqGfvfrG7wvYV3x2WJDWK4iYNAABQdwiBAFDH/H6/bJERSm3uKHfs1clDdfWA1IC2xrEs\nBQEAAOoOIRAA6ti+o06Venxq2jgmoP2y7i1ksVh0y+UdA9qvz2hXj9UBAIBwQwgEgAv0+fqDWr/9\nzDN+pR6fSj0+5Re59eWmI3rtw62SpFZNYwOu+8XVF0mSLGfNHDrnwQzFRtsEAABQV3jwBACqae+R\nAlksUmrzOEnSgo+3KT7Wrl5pyfL6fLrvmWWSpBsHt9d7Zz3/1zwxRg/ekq7n39mg8SMuCVjy4en7\nB8oaYVE8E8IAAIA6RggEgGqa8drXahQVqbkPDVGpxydJyi9061hekWJjzozivXfOBDApzRxKbR6n\n+VOuLPeayQkx5doAAADqAreDAkA17DhwQpJU5PJIkkrcHuPYlJe/1Prtxyu99vTIIQAAQDDVaCTw\nn//8p959911Jksvl0pYtW/TWW2/pvvvuU7t27SRJo0aN0jXXXKPFixdr0aJFioyM1Lhx4zR06FCV\nlJRo0qRJysnJUWxsrJ5++mklJSUpKytLM2fOlNVqVUZGhsaPHy9Jmjt3rpYtW6bIyEhNnTpV6enp\ntfPuAaAavD6f/vj6uoC2YpcnYH/+/5VfBF6SruqXWmE7AABAfatRCBwxYoRGjBghSZoxY4Zuvvlm\nbdq0SXfddZfGjBljnJedna0FCxZoyZIlcrlcGj16tAYNGqSFCxcqLS1NEyZM0AcffKB58+bpscce\n0/Tp0/XCCy8oJSVF9957rzZv3iy/3681a9bo7bff1uHDhzVhwgQtWbKkdt49AFTDhp05Afs+n19T\nXv6yStdmpLesi5IAAACq7YJuB/3uu++0c+dOjRw5Uhs3btSyZct0xx13aOrUqXI6ndqwYYN69eol\nu92uuLg4paamauvWrVq7dq0GDx4sSRoyZIhWr14tp9Mpt9ut1NRUWSwWZWRkaNWqVVq7dq0yMjJk\nsVjUqlUreb1e5ebm1sqbB4Dq+N9Ts3yeVlBcWuF5dlvZR2vTxtGaP+VKvTp5aLmZQQEAAILlgiaG\nefnll/XAAw9IktLT03Xrrbeqe/fueumll/Tiiy/qoosuUlzcmWdgYmNj5XQ65XQ6jfbY2FgVFBTI\n6XTK4XAEnLt//35FRUUpISEhoL2goEBJSUkXUjoAVJvznND375VnJn6557qL9cr7myVJ8x66XCec\nLiXGlS36fvYSEAAAAMFW4xCYn5+v3bt3a8CAAZKkYcOGKT4+3th+8skn1bdvXxUWFhrXFBYWKi4u\nTg6Hw2gvLCxUfHx8QNvZ7TabrcLXOJ/ExEaKjLRW+f0kJzNhg5nQX+YSKv3VMy1ZWduzdUXvNlq2\n7oA+X3dQktShVWNdO6STikp9GpTeSs2bx6l58yAXewFCpb/CBf1lLvSXudBf5kJ/VV2NQ+DXX3+t\ngQMHGvtjx47VtGnTlJ6ertWrV6tbt25KT0/XnDlz5HK55Ha7tWvXLqWlpal3795avny50tPTlZmZ\nqT59+sjhcMhms2nfvn1KSUnRihUrNH78eFmtVs2aNUtjx47VkSNH5PP5qjQKmJdXVOX3kpwcp+zs\nghp9H1D/6C9zCaX+KixyyyIp3+kKaG/aOEq5OU79qGcrSTL1+w2l/goH9Je50F/mQn+ZC/1VscqC\ncY1D4O7du9WmTRtj/4knntCTTz4pm82mpk2b6sknn5TD4dCdd96p0aNHy+/366GHHlJUVJRGjRql\nyZMna9SoUbLZbJo9e7aksklmHn30UXm9XmVkZKhHjx6SpL59+2rkyJHy+Xx6/PHHa1oyAFwQd6lP\ndptV6R2baN32bKP9f37SJYhVAQAAVI/F7/f7g11EXajOXwL4y4G50F/mEkr99dtXvpSzuFTPTcjQ\n3U9/LkmaeU9/tWwSOpO+hFJ/hQP6y1zoL3Ohv8yF/qpYrY8EAkC4cZf6ZI+MUITFoud/NVhHc4tC\nKgACAIDwQAgEgLMUuzzK2nFch3IKFW236tqB7YxjpR6vYmNskiRHjE2O1o2DVCUAAEDNEQIB4Cx/\n/2ir1mw5ZuwP75eqSGvZun/5RaWKj7UHqzQAAIBacUGLxQNAKPH7/QEBUJKO5pbNNHz01IzDzRMb\n1XtdAAAAtYkQCACnHMktv7RMidsrSZrz9gZJUtsWrEEEAADMjRAIAKcczC4s11bs9sjj9Rkjgolx\nUfVdFgAAQK0iBALAKTsPnizX5nL79P2hfGO/V+fk+iwJAACg1jExDACcYrVayrVlfntIzZNiJEl3\nDEtTo2g+NgEAgLkxEggApxSVeCRJv/1ZH428spMk6bvvc/TpNwckSZd0bBK02gAAAGoLf9IGEPY8\nXp+WrT+o5VmHJEnJjWNUUFRa7rzkxtH1XRoAAECtYyQQQNj7/lC+3vx0h7EfGxOp9A5N1CUlIeA8\ni6X87aIAAABmw0gggLCXteO4sR1ptcgaUfb3scl39JYkbd9/gkXiAQBAyCAEAgh7e48WGNser7/c\n8bRzRgQBAADMjNtBAYQ1r8+nLXvzjP2ubRODWA0AAEDdYyQQQFjbsCvH2J79wCA15rZPAAAQ4giB\nAMLS4ZxCWSMs+vtH2yRJVw9IVWJcVJCrAgAAqHuEQABhpdTjlSS9+O5GWSMsyi90S5LSO7AGIAAA\nCA+EQABhw+/3675nlld4rDOTvwAAgDDBxDAAwsZn6w5W2N6+ZbwiWAMQAACECUIggLCxbnt2he1H\ncovquRIAAIDgIQQCCBv9ujarsP2xn/Wp50oAAACChxAIIGxUtBB8pNWilk1ig1ANAABAcDAxDICQ\n5/f7tWbLMf175e6A9rbN43TfDd2CVBUAAEBwEAIBhLwX390Y8DzghJsv0SUdmijSys0QAAAg/PAb\nEICQkV/klt8feMuns7i03IQwiXFRBEAAABC2+C0IQEhYtv6gJj6/Qm9+skOSdDinULn5Jfr0m/3l\nzm3bPK6+ywMAAGgwuB0UQEhYnnVIkrR03QHdOrSjfvvKVwHHbxrSQZ1bN5bX75eFNQEBAEAYIwQC\nMJXjJ4tV4vaqTbIjoH3v0QJj+4sNh8td1yUlQWkpCXVeHwAAQEPH7aAATMPv9+vXL63W46+u+cHz\n3vhke7m2KJu1rsoCAAAwFUIgAFPIK3Bp8l9WG/vvLNtlbJ87GUxFYqIIgQAAABIhEIBJvLBkg46f\nLDH2tx84YWzn5rskSXZbxR9pP+rdRs0SG9VtgQAAACZBCATQIOTml8jj9amgyK0D2U4Vuzwa+/Rn\nWrK8bMRvz5GCgPN/PaqXJOnVDzZr0kurJEnpHZtKkhwxNl2UWvb839hru+qOn6TV19sAAABo8JgY\nBkC9yNp5XH/510bNGNNPzc8ZlTuWV6QpL3+p9I5NtGFXjiSpb5dk+f3SB6v36qYhHQLOf+XXV8ga\nEaHc/BKt/O6I0X5x20QN7dlKLZrEKjEuSiecLiU4our+zQEAAJgIIRBAvXj1P5vlLvXpk6/3639+\n0sVoP3S8UGu3HZMkIwBK0jfbzizwfvfTnxvbf51UFgD/mblL/1m1N+BrZKS3DFgEngAIAABQHiEQ\nQL0oLPFIkj5bd9AIgYUlpXrsb1/90GUBenVuqkhrhCbNW6Wc/JJyx88OgAAAAKgYvzEBqFdnr++3\neU9eta699/puklRhAOzePunCCgMAAAgTjAQCqHPffX/mNk9HTNnHTlFJqV56b+MPXvfH+wboNy9/\nKUl64q5LK1zr76bB7dW1bZLaNIutxYoBAABCFyEQQK0qcXv0ydf7dXnP1oqPtUuSXvtw61nHvZKk\nA9mFAdddelEzfb31WECbRdJ1l7WTx+tTavM4o336Ly6V1+dX41i7kuKjZLFY6ujdAAAAhB5uBwVQ\nqzbvydO7X+zWmi1HJUknClzKK3AZx32+soXdV286M6vnM7+8zAiAvdOSjXa7zaqbhnTQrUM7BXyN\nti3i1KFVvJo0jiYAAgAAVBMjgQBq1Sdf75ckHTxeNtL36POZxrEou1WlXp92HDih5VmHJEl3fJNb\n4AAAIABJREFU/iRNSfHRxjnWCItm3tNf+485md0TAACgDjASCCDAwWynVm88cv4Tz1Hq8eqV9zdp\n2/4TkiSv16/P1x3Q0dwi4xyX26vDOUX64+vrjLbLe7WWJF3VP1WS1KlNY7VsEqt+XZtfyNsAAABA\nJRgJBBBg2qtrJEmfrt2vkVd2VlpKQpWu++/X+7V601FjP6+gRCu+O2zs/+LqiwKeDZQki0WKOHU7\n502DO6hdizj16Nj0Qt8CAAAAfgAjgQAqtPtwgZ56Y935Tzwla8fxgP1N5yz/MKRHK6U2dwS0zX5g\nkLFti4xQv67NFWUvPwMoAAAAag8hEIDB5/eXaysqKa3StbsO5Vd6bNa4yyRJT9zVTw/d1kOSdM2A\ntjzzBwAAEASEQACGhZ/sKNc2fs4XOpjtPO+1jhhbhe0Pj+6tJo3PTPxySYcmmj/lSt1yRceaFwoA\nAIAaIwQCMJy9bMO1A9sa29NeXaMxT32mwzmFFV0mSYqNLnvEeNSPOwe0D+2TUstVAgAA4EIQAgEY\nilweSVJ8I5sGXdKy3PHfvvKV/BXcMipJkZER6tS6sYb1PRP6Mip4DQAAAAQXs4MCYabU45M1wqKI\niMoXWbdFWhVlq3iCloKiUsXH2o19j9endduzdcOg9sbEL4+M7KnP1h3QncO71G7xAAAAuGCEQCCM\neLw+3ffMMnXvkKSHb+sZcMxZfGYCmF/e1F2JcVG6+fIO2ro3Tx6v31j/z13qDbju3lnLjO1hfVM0\n6sed1a19krq1T6q7NwIAAIAaIwQCYWTbvrIgt/H73HLHPvpqnyTJbotQ+5bxkqRrB7bTtQPbSZL+\n8fE2LVt/UC6PT5Lk9fnKrft3NK9IAAAAaNh4JhAII3uOVL6Mw/GTxZKkLimJFR63R5Z9XJweCfxg\n9V6t/O5IwDkHqjCLKAAAAIKrxiOBN910kxyOsud/2rRpo/vvv19TpkyRxWJR586dNX36dEVERGjx\n4sVatGiRIiMjNW7cOA0dOlQlJSWaNGmScnJyFBsbq6efflpJSUnKysrSzJkzZbValZGRofHjx0uS\n5s6dq2XLlikyMlJTp05Venp67bx7IMy4Sn3GdlGJR42iI+Xx+uQu9enidklas+WYxlxzUYXX2k89\nI3g6BBaVeMqd86PebeqgagAAANSmGoVAl8slv9+vBQsWGG3333+/Jk6cqP79++vxxx/X0qVL1bNn\nTy1YsEBLliyRy+XS6NGjNWjQIC1cuFBpaWmaMGGCPvjgA82bN0+PPfaYpk+frhdeeEEpKSm69957\ntXnzZvn9fq1Zs0Zvv/22Dh8+rAkTJmjJkiW19g0AwoXf79d/Vu0x9sfPydRtQzvp/VV7VOzy6PQ0\nMbbIim8QiLKdGgn0+OTz+7Vue3a5c67qn1rbZQMAAKCW1SgEbt26VcXFxRozZow8Ho8efvhhbdq0\nSf369ZMkDRkyRCtXrlRERIR69eolu90uu92u1NRUbd26VWvXrtXdd99tnDtv3jw5nU653W6lppb9\nEpmRkaFVq1bJbrcrIyNDFotFrVq1ktfrVW5urpKSmHQCqI69RwvKtS3+fKexfXrhhyh7xbOC2iPL\n2p9b/G1A+7C+KbpuUDtFWi2yWCqfcRQAAAANQ41CYHR0tMaOHatbb71Ve/bs0T333CO/32/8Ahgb\nG6uCggI5nU7FxcUZ18XGxsrpdAa0n33u6dtLT7fv379fUVFRSkhICGgvKCggBALVdPxESZXOs0ZU\nPBJot1Xcfu7i8AAAAGjYahQC27dvr7Zt28pisah9+/ZKSEjQpk2bjOOFhYWKj4+Xw+FQYWFhQHtc\nXFxA+w+dGx8fL5vNVuFrnE9iYiNFRlY8olGR5OTzvyYaDvqr+uY99dl5z4mPtVf6ve3VtYX+/tG2\ncu1V6Qv6y1zoL3Ohv8yF/jIX+stc6K+qq1EIfOedd7R9+3Y98cQTOnr0qJxOpwYNGqSvvvpK/fv3\nV2ZmpgYMGKD09HTNmTNHLpdLbrdbu3btUlpamnr37q3ly5crPT1dmZmZ6tOnjxwOh2w2m/bt26eU\nlBStWLFC48ePl9Vq1axZszR27FgdOXJEPp+vSqOAedWYqj45OU7Z2eVvlUPDRH9VX/aJYmP7zuFd\nZLNGaM+RfH227qAeGdlTB7OdWvTZTj10a49Kv7eNIs/c6mmLjFCpx6frB7U7b1/QX+ZCf5kL/WUu\n9Je50F/mQn9VrLJgbPH7/f4Kj/wAt9ut3/zmNzp06JAsFoseffRRJSYmatq0aSotLVWHDh30+9//\nXlarVYsXL9Zbb70lv9+v++67T8OHD1dxcbEmT56s7Oxs2Ww2zZ49W8nJycrKytIf/vAHeb1eZWRk\n6KGHHpIkvfDCC8rMzJTP59NvfvMb9e3b97w1Vud/Av6nMRf6q/qWLN+lD1bvlSTNn3KlJMlV6tXh\nnEK1axFf5dfxeH06frJELZIaVfka+stc6C9zob/Mhf4yF/rLXOivitVqCDQDQmDoor+q770vvte/\nV+7RwyN7qHv7JvX6tekvc6G/zIX+Mhf6y1zoL3OhvypWWQhksXggDLhOre0XG20LciUAAAAIthov\nFg+g4TuQ7dSm3bnGIvH2StYABAAAQPggBAIh7PFX1wTsR9mqPmMuAAAAQhPDAkAYsVeyEDwAAADC\nByEQCDEut1dFJZ4Kjzl4JhAAACDscTsoEGImvbRKzuJSDe3VOqD951d1UUSEpZKrAAAAEC4YCQRC\njLO4VJL0+fqDAe3tW1Z9PUAAAACELkIgEAZioyPVqmlssMsAAABAA8DtoEAIyStwlWubNKqXurZN\nDEI1AAAAaIgIgUAIOeEMDIF/mzxUERaeAwQAAMAZ3A4KmExegUvO4lLl5pfozU+3q7Ck1DhW4joz\nK+iAi5sTAAEAAFAOI4GAyTzy4sqAfb9PuuMnaZKkolMhsE+XZN05vEu91wYAAICGj5FAwETcpd5y\nbV9uPmJsnw6B6R2bKCaKv/EAAACgPEIgYCJvL9tVrq3wrIXhSz0+SVKUzVpvNQEAAMBcCIGASfh8\nfuWcLKnwWPaJYkln1ga0RvCjDQAAgIrxmyJgEnP/+Z2ydh439hvH2tW9Q5Ik6f1Ve+Tz+3Uwu1BS\n2bqAAAAAQEUIgQgrufkl8vv9wS5DkuTx+rTr4Mkqnev3+wMCoCR1aBWv7u2bSJJWbDis977YbRzr\nnNK49goFAABASCEEImT5/X698cl2bduXJ2dxqVZvOqJH563S2m3ZwS5NkvTGJ9s1c8Fa/ePjbec9\n1+P1BezfMSxNd13TVUN6tDTadh44IUka1jeF20EBAABQKe4ZQ8jasjdPS9ce0NK1ByRJ1oiyNfO+\n+z5HfS9qFszSJElrthyVJC1bf1A/O2c5h2+2HlNBcamG9motSXKVBobAvhc1kyPGJkm6/UedtWjp\nDm3dVxYCL2qbUNelAwAAwMQYLkDIOn7OJCpeX9ltoKfDYLC1axFvbK/fkW1M6iJJ897bqAVnjRCe\nnvXztCjbmR/d/l0DA+0lHZrUdqkAAAAIIYRAhJTMbw9p4/c5kqTjJ4srPGdZ1iE5i0vrs6wKlbjP\nrPn3wpLvtODjbTp2IrBm36nnF89dH9AeeWYJiMaOqIBjkVZ+rAEAAFA5bgdFyPD7/Xrtw62SpPlT\nrtR/Vu2t9NwH//yFJOnVyUNlsdTPyOCr/9mslRuP/OA5B7OdapYQY+zf/fTnmj/lSrlPjQT27NRU\n1wxoq4hzRjNvHNw+YGIYAAAAoDIMGSBknHvLZFUcyS2q9Ji71KsPVu/RCafrAqo643wBUCobESx2\neQLaXKVeTZ+/RpLUskkjdWpTfubP6we113PjB+m58YNqpVYAAACELkIgQkbRWeHp9GQwp40Y0kFP\n3z9Q8x4eEtB+OlxV5JNv9mvJ8u/18r821W6hZ7lmQNtybfM/2BKw/7//d2bfFln5j2xjR1S5W0MB\nAACAcxECYXpen0+frTugwpIzIfCNT7Yb2zdf3kFX9U9VckKMou2RxqyakuTxVr5m4Pb9ZWv4bdt/\nolr1LF17QNureM3Abs3Lta3dHriExZotx4ztgqLgP8sIAAAAcyMEwvQWfLxNr/93u6b97atyx0b9\nuLOuHdguYLKUGWP6adKoXsb+mKc+U15B4C2fJwvd+u7UBDPnU+rx6kC2U1LZc4mLlu7QO8t2BZyz\nupJbQZPio3X3T7tq8uhe+uukK877ta7s06ZKNQEAAACVYWIYmNrR3CJlfnu40uMdW5V/fi4xLkqJ\ncYG3TT7y4krNn3Klsf/RV4GTyjiLSwNGEM/2y2cz5fX5NXl0Ly1Z/r28Pr92HjwZcM4r/9lc7rq/\nTrpCkdYIXdb9zILv7VrEac+RAklStN0aMIPo2fUBAAAANcVIIEwtNz9wLcAou1V3DEsz9hMc9iq/\n1rx3vzO2P16zP+DY5j25FV5TUOQ21h/ctCc3IPydXtbB76/4ltOKlnK49YqOkqSfDe+ieQ9ffta5\nDWNtQwAAAJgfI4EwtVZNYwP2XW6vftSnjRIcdhW7vEqKj6702hcfGqLNe/L094+2yllcqm+2lT2L\nd/atoUN6tFLmt4cCnjc826+eX2Fsn7skxf2zl2v+lCt1wuk22u756cVq08xR6ahi13ZJmjtxsGKi\nAn80p/380krfBwAAAFAdjATC1ErOWUR96p19JEl9ujRTRnrLii4xxERFqk+XZD16e0+jzeX2Bkzq\n0qdLsqSy5w7PVdkI37lOL/lwRc9WGti9hVKaOcrdjnq2RtE2Y+3CR27vqav6p6pNcmyl5wMAAADV\nwUggTC0z65AkafovLlXbFnE1eo3U5nHq17WZ1mw5pu0HTujlf5ctCXHPTy+udMROqnxdwsS4KGM0\nce+RAs147WtJ0v5Tk8dUR7d2SerWLqna1wEAAACVYSQQppZfVHarZUyUtVZe77nF3xrbrZNjldrc\nUem5Z0/acraRV3Yytk8HQElqkdioFioEAAAALgwjgTClopJSjZ/zhbHviKn6BDAVSW0eZ6zH1//i\n5rp2QFu1aVZxACwqKdX8/9uqdWet5zdjTD9FWi06klOkXmnJOul0a+HSHcbxGzLa64aM9hdUIwAA\nAFAbGAmEKW3Zmxewf6EjgWc/P/jV5qP6dtfxcueMeeozSdL6HccDAuCwvilKaeZQyyax6pVW9gzh\nsEtTAq69flC7C6oPAAAAqC2MBMKUIiICl0w4PZFKTcU3sutnw7voH6cmgKlszpfTQfBsTeIrn+RF\nkqwRlguuDwAAAKgthECYSonboxXfHgyYLGXq//Splde+oldrffd9jtbvOK6LUhON9rMneqlIxzbl\nF6SXyhaD/3ZnjtI7MrELAAAAGg5CIExlzZZjeu3DrcYSCwO7NVe7ljWbFbQi917fTUdzi5Ta/Mxr\nuiqZAOa09i3jK2yPtEYYS0wAAAAADQXPBKLBWJZ1UGOe+kzu0spDV/aJYklnFnS/flB7RVpr73/j\nKJs1IABKkquSevp0SdYVvVorgls9AQAAYCKEQASdz+/Xe198r398VPY8XtbO8pOynPbB6r0B+80S\nY+q0NkmaPLq3Gjvsuv+GbgHtD9x0iX42vEudf30AAACgNnE7KILuwy/36t8r9xj7Cz/dof3HnLr5\n8o7nvbY+Jlzp1KaxnhufIUkqdnn094+21eroIwAAAFCfCIEIuiXLvw/YP1no1ger95YLgQ/PXSFJ\nuvHyjrqkXaJaJNX/4uux0TZJ0uCzlpQAAAAAzIThDARVsctT6bHlWQeN7Q9W79EJp1uS9H+r9qh9\ny3jFRNX/3zD6XtRMU/+nj0Ze2anevzYAAABQGxgJRFBVNumKJP39o21ye3wa1jclYLTwibsH1Edp\nlepUyZIQAAAAgBkwEoig+qGZQKWy5wPPdu91F+uSTk3rsiQAAAAgpBECEVRuj0+S1LNTU025o3eF\n55yeLXRIj1Ya0K1FvdUGAAAAhCJCIILq9O2gLZIaKS0lQX0vaqbu7ZMCAuHz72yQJHVsVfGi7AAA\nAACqjmcCEVQlrrIQGBNllST98sbulZ6bX+Sul5oAAACAUMZIIIJqxXeHJVW83t/EW9ON7V+P6qXh\n/VLrrS4AAAAgVDESiKByFpdKUoVr/qV3bKon7+6v4hIPM3ICAAAAtYQQiKBqHGuXJLVrEVfh8dZN\nY+uzHAAAACDkcTsogur0SGBsjC3IlQAAAADhoUYjgaWlpZo6daoOHjwot9utcePGqWXLlrrvvvvU\nrl07SdKoUaN0zTXXaPHixVq0aJEiIyM1btw4DR06VCUlJZo0aZJycnIUGxurp59+WklJScrKytLM\nmTNltVqVkZGh8ePHS5Lmzp2rZcuWKTIyUlOnTlV6evoPVAezcBaXKjffpZgoq2KiGJQGAAAA6kON\nfvP+97//rYSEBM2aNUsnTpzQjTfeqAceeEB33XWXxowZY5yXnZ2tBQsWaMmSJXK5XBo9erQGDRqk\nhQsXKi0tTRMmTNAHH3ygefPm6bHHHtP06dP1wgsvKCUlRffee682b94sv9+vNWvW6O2339bhw4c1\nYcIELVmypNa+AQiOrB3H9fySDbJI+km/lGCXAwAAAISNGoXAq666SsOHD5ck+f1+Wa1Wbdy4Ubt3\n79bSpUvVtm1bTZ06VRs2bFCvXr1kt9tlt9uVmpqqrVu3au3atbr77rslSUOGDNG8efPkdDrldruV\nmlo2A2RGRoZWrVolu92ujIwMWSwWtWrVSl6vV7m5uUpKSqqlbwGCoaTUI0nyS+raNjG4xQAAAABh\npEbPBMbGxsrhcMjpdOrBBx/UxIkTlZ6erl//+td64403lJKSohdffFFOp1NxcXEB1zmdzoD22NhY\nFRQUyOl0yuFwBJz7Q+1oOA7nFOpIblG1rvls7UFje87bG2q7JAAAAACVqPGDWIcPH9YDDzyg0aNH\n67rrrlN+fr7i4+MlScOGDdOTTz6pvn37qrCw0LimsLBQcXFxcjgcRnthYaHi4+MD2s5ut9lsFb7G\n+SQmNlJkpLXK7yc5+fyvifJyThbrt698JUl690/X6Yusg+p3cYvzTvSy8+BJY7tZUqNqf//pL3Oh\nv8yF/jIX+stc6C9zob/Mhf6quhqFwOPHj2vMmDF6/PHHNXDgQEnS2LFjNW3aNKWnp2v16tXq1q2b\n0tPTNWfOHLlcLrndbu3atUtpaWnq3bu3li9frvT0dGVmZqpPnz5yOByy2Wzat2+fUlJStGLFCo0f\nP15Wq1WzZs3S2LFjdeTIEfl8virdCpqXV/WRqeTkOGVnM7pYE+998b2x/fmavXr+nQ0a9aPOGnZp\n5c/5+f1+Y7tj63iNuaZrtb7/9Je50F/mQn+ZC/1lLvSXudBf5kJ/VayyYFyjEPiXv/xF+fn5mjdv\nnubNmydJmjJliv7whz/IZrOpadOmevLJJ+VwOHTnnXdq9OjR8vv9euihhxQVFaVRo0Zp8uTJGjVq\nlGw2m2bPni1JmjFjhh599FF5vV5lZGSoR48ekqS+fftq5MiR8vl8evzxx2tSMupIckKMsf38O2W3\nde46dFLDVHkIPJh9ZmT3t3f2rbviAAAAAJRj8Z89LBNCGFmqHws/3aFPvtkf0NYkPlqzfnlZQFup\nx6dSj0+NoiO1eU+unlmUpX5dm+n+G7pX+2vSX+ZCf5kL/WUu9Je50F/mQn+ZC/1VscpGAlksHhfk\n3AAoSTn5JXKXegPaXnpvo37396/l8/v194+2SpI6t0molxoBAAAAnEEIRI3lFbgqPfb+qj0Bz/5l\n7TyuY3nFmv/BFmWfKJEktW3Ow7sAAABAfSMEosacxaWVHvtg9V6998Xucu2rNh6RJEXZrOrUpnGd\n1QYAAACgYoRA1Ni5t3xK0t8mDzW231+1R4UlpRrz1GflznNVcC0AAACAulfjdQIRGlxur95Ztkud\nUxpredYhbdmbp3t+erEGdm9x3muLXR5JUrsWcdpzpECtmsYqwmIJOGfCnC/qpG4AAAAANUMIDFMn\nC93asOu4ck6WaOm6A1q67oBx7JX/bNaAbs1lOSfQnWvDrhxJ0qBLWmrklZ3UOtlR5a/fmVtBAQAA\ngKAgBIapj77aq4/XlJ/Z87Qte/N0cbskSVKpx6u127PVq1OyouxWLVq6Q44Ymz5dWxYcmyfGqEtq\nonHtzHv665utx/RuBc8EStIjI3sqLYUQCAAAAAQDITBM5eRXPrOnJK3fftwIgVk7c/TXf2+WJD19\n/0D99+vA8Ni9Q5OA/ZZNYnXdoPaKj7Xr7x9tkyTNnThYf/9om356WTulNKv6iCEAAACA2kUIDAPH\n8or05qc79POrLlJiXJQk6bLuLfTN1mPlzk1p5tD+Y04tXXdAqzYd0cgrO6moxGMcn/yX1VX+uhnp\nLeX2+NSva3M1irZp3I3VXxgeAAAAQO1idtAQdzinUFNe/lIbduXokRdXGu3Pv7Oh3Lk9OzXVgG7N\njf1il0dvfrpdiz/fWenrT//FpZUes0ZEaFjfFDWOtdewegAAAAC1jRAY4mb879fl2rw+X8D+IyN7\nym6L0M+vvkidWgc+q+cuDTz3XG1bsOA7AAAAYCbcDhri3J7AEHfumn0/v6qLurVP0l8euUKS1DjW\nrmYJMTp2orjca3XvkKSN3+eqscOuWy7vqCibtc7qBgAAAFA3CIEhKL/IrYnPr1DLJo1+8Lzfjemn\nNhVM0jLhlnRN+9tXAW1T7uitlGYO7ThwUpd0SDrv8hEAAAAAGiZCYIjweH3679f71SwhRvPe2yhJ\nOpxTVOn5jWPtFQZASWrdNFbzp1wpn8+vIpdHXp/feK4vvWOTCq8BAAAAYA6EwBDxq+dXqNjlqfDY\nTUM6aOV3h3Us78wtnk/dP/C8rxkRYZEjxlZrNQIAAAAIPkJgiKgsAEpS/4ub67rL2tVfMQAAAAAa\nLGYHDRHWiMBn9K4ekGps2yPpZgAAAABlSAchIsERFbA/4OIWxnaklW4GAAAAUIbbQU1s6doDeuOT\n7RrUvYVK3IG3gyYnRBvbPNcHAAAA4DRCoEkdP1msNz7ZLklaufGIJKlb+ySNHNpJyQkxirJb9fKj\nl8sawSggAAAAgDNICCa1eU9eubZNu3PVpplDUfayRdxtkVZFRLCeHwAAAIAzCIEm9dqHW8u1nTs5\nDAAAAACcixAYQp4dPyjYJQAAAABo4Hgm0KSi7Fa53F49O36Q4mPtskiyWBgJBAAAAPDDCIEm5fP5\n1b5lfLmlIQAAAADgh3A7qAm5Sr0q9fgUE2UNdikAAAAATIYQaEL7jhZIklo3dQS5EgAAAABmQwg0\nocM5RZKklk0aBbkSAAAAAGZDCDSh08tDeH3+IFcCAAAAwGwIgSbGM4EAAAAAqosQaBIut1e/ev4L\nrdly1Ggb0K1FECsCAAAAYEYsEWECBUVu/er5FZKkv/xrkySpZ6emimBdQAAAAADVxEigCUx7dU25\ntjbNmBkUAAAAQPURAk0gv9Bdrq17+6QgVAIAAADA7AiBJtWhVXywSwAAAABgQoTABiznZInGPPWZ\nsX/6GcCHR/ZQpJWuAwAAAFB9TAzTgE16aVXA/t8mDw1SJQAAAABCBcNJDVSpxxewz5qAAAAAAGoD\nIbCB2rI3N2C/2OUNUiUAAAAAQgkhsIE6nFMkSWqR1EiSWBMQAAAAQK3gmcAG6q3PdkqSJtx8iT5b\nd1CD01sGuSIAAAAAoYAQ2AC99uEWY7tFUiPdMSwtiNUAAAAACCXcDtrAlLg9yvz2sLFv4TZQAAAA\nALWIENjAbN6TZ2xfM6BtECsBAAAAEIoIgQ3M3H9+Z2ynd2wSxEoAAAAAhCJCYANystBtbCfGRSkt\nJSGI1QAAAAAIRYTABuT1/24ztru3TwpiJQAAAABCFSGwAck5WWJsj7yycxArAQAAABCqCIENSExU\n2YodD9/WQ42iWb0DAAAAQO0jBDYgW/aWzQzavQMTwgAAAACoG4TABqLU4wt2CQAAAADCACGwgTiW\nVyRJ6pOWHORKAAAAAIQyUzx45vP59MQTT2jbtm2y2+36/e9/r7ZtQ2ch9aN5RZr26hpJUsumjYJc\nDQAAAIBQZoqRwE8//VRut1tvvfWWHnnkET311FPBLqnGcvNLtOvgyYC2P725/qw9S/0WBAAAACCs\nmGIkcO3atRo8eLAkqWfPntq4cWOQK6q+k06XnnpjnY7mFUuSbhzcXtcPaq/Mbw8pr8BlnHfdZaEz\nwgkAAACg4TFFCHQ6nXI4HMa+1WqVx+NRZGTl5ScmNlJkpLXKXyM5Oe6CajyfmNgoIwBK0ntf7Nbg\n3il67cOtRtu1g9qrVcuEOq0jVNR1f6F20V/mQn+ZC/1lLvSXudBf5kJ/VZ0pQqDD4VBhYaGx7/P5\nfjAASlLeqYlWqiI5OU7Z2QU1rq+mHvlzZsD+jYPaBqUOswlWf6Fm6C9zob/Mhf4yF/rLXOgvc6G/\nKlZZMDbFM4G9e/dWZmZZYMrKylJaWlqQK6p9bVvEyRphiu4AAAAAYGKmGAkcNmyYVq5cqdtvv11+\nv19/+MMfgl1SjUwe3Uufrz+oYZemaOY/1gYc+0nflCBVBQAAACCcmCIERkRE6He/+12wy7hgXVIT\n1SU1UZLUJD5aOfkl+lHvNrp1aEfZbVV/fhEAAAAAasoUITAUzbynv/YcKVBaChPBAAAAAKg/PIQW\nJHablQAIAAAAoN4RAgEAAAAgjBACAQAAACCMEAIBAAAAIIwQAgEAAAAgjBACAQAAACCMEAIBAAAA\nIIwQAgEAAAAgjBACAQAAACCMEAIBAAAAIIwQAgEAAAAgjBACAQAAACCMEAIBAAAAIIwQAgEAAAAg\njBACAQAAACCMEAIBAAAAIIwQAgEAAAAgjBACAQAAACCMEAIBAAAAIIwQAgEAAAAgjBACAQAAACCM\nWPx+vz/YRQAAAAAA6gcjgQAAAAAQRgiBAAAAABBGCIEAAAAAEEYIgQAAAAAQRgiBAACIgfBsAAAP\n6ElEQVQAABBGCIEAAAAATIGFDWoHIRBAreGDGah9RUVFKiwsDHYZABB0J06c0PHjx4NdRkgIixB4\n4MABY5tfUhu+PXv2GNv0V8P38ssva86cOZIki8US5GpwPl999ZWWLFkiiZ8vM3j99df18MMPa9u2\nbcEuBVXwxhtv6M0339SWLVuCXQqqYMGCBZo/f742bdoU7FJQBe+++66GDx+uRYsWBbuUkBDSIXDl\nypUaO3asnn/+eT3zzDPav3+/LBYLv/g0UKtXr9aYMWM0e/ZsPffcczp27BihogFbvny5fvnLX2rR\nokVKSEiQRKgwg48//liffPKJjh8/zudhA+X3+5Wbm6urr75aOTk5euaZZ9S7d++A42hYnE6nxo0b\npy1btighIUF//vOftXz5ckmSz+cLcnU4V1FRkR588EFt2bJFUVFRmj9/vnbt2hXsslCJ9evXa+zY\nscrKylL37t2VkZEhic/CCxXSIXDhwoW6+eab9ac//UlJSUmaOXOmJEYrGqqFCxdqxIgR+uMf/yin\n08kHcgP2z3/+U++8845+9atf6ec//7nxQczPVsPj9XqN7czMTG3btk2tW7fW66+/Lok+a2i8Xq8s\nFouSkpLUsWNHtW3bVvPmzdNjjz2mWbNmSaLPGpLTP19er1dxcXGaNGmSrrnmGl1zzTX685//LEmK\niAjpX7VMqbS0VNHR0Zo2bZpuv/122e12ORyOYJeFSuzbt0/33XefZsyYocGDB2vHjh2S+Cy8UNYn\nnnjiiWAXUVuKi4u1ZcsWWa1WSWW/8Fx//fVq3LixmjZtqldeeUVdunRRSkqK/H4///MEWXFxsbZt\n22b017Zt2/T/7d19TJX1/8fxJxw4BAdvDhwPotyDHglUHIr3ZtNQENTSohtzpanTdLhy05Zu1mZU\n05a2+QdZacnECklDJ4tUICTRtHkLuakJ3qHTI6DiKeD3x3cxv9/ft9T8/bw4ndfjP27O9v7stc91\nrvf1ua7PlZmZCcDq1asZMGAAJpMJq9VKa2ur8jLYH/Orc+fOOBwOMjMzsdlsnDx5ErPZTEJCguZV\nB9Lc3ExOTg6HDh3iypUrOBwOLBYLoaGhjBw5kpKSEkJDQwkJCVFuHcCdeV2+fBmHw0FTUxN5eXkM\nHz6cadOmsWHDBi5evMigQYN0TDTYnXk1NDRgs9koKSkhKSkJq9VKU1MT5eXlmM1m4uPjNcc6gPz8\nfI4ePUpiYiKXLl0iIiKCqKgocnNz2bx5Mw0NDZw8eZLk5GTNrw4gPz+fw4cP07dvXxwOB2FhYbS0\ntFBQUMCgQYMIDw9XTg/oH3N5qqysjMmTJ5Ofn8+sWbPw9/fH19eX4uJi6urqOHbsGGPGjGHbtm2A\nrh4Ybc+ePTz55JN88803ZGdn43Q6yc7OxmazUVxcTHx8POfPn+f5558HdCXVaH/ktW3bNmbOnPlv\nD2XX1dW1X5WTjqG5uZk1a9bg7+/P+PHjWbduHaWlpVitVsaOHUuPHj1ISkpi69atgI6HRvvPvD75\n5BMqKiqIjIxk+vTpTJo0iaCgIJYvX05JSQkul0vHRAPdmde4ceNYu3Yt586dIzQ0lA0bNrBixQry\n8/OZPHkyNTU17au7Yqz9+/eTm5vLrVu3iIyMZPDgwQCMGDGCiooKXnzxRfLz82lubtb86gD279/P\nunXruHXrFl5eXrhcLkwmE1FRUezcuRPQueGD+kesBLpcLtatW8fMmTOZMWMG33//PX5+fqSlpXHm\nzBm+/PJLnE4nGRkZ3Lp1q/3ZCh2UjfHbb7/x8ccf88orrzBt2jTq6+spLS2lb9++WCwWEhMTGTdu\nHAMGDODo0aP069ePTp06GV22x/rPvK5cuUJ5eTl9+vQhMDCQbt26UVZWxuDBg/H39ze6XI92+fJl\nLBYLXl5e5ObmMnfuXHr37k1gYCA//PAD4eHhBAcH4+vrS2BgIGVlZbhcLhwOh9Gle6Q/yysgIIB9\n+/YxYMAAhg8fjtPpxGKxcOTIESwWC0OHDjW6dI/03/JyOBz4+/tTVVVFeno6o0aN4sKFC8yaNQun\n00nnzp1JSEgwunSP9EdeACdPnqS6uhqTyURNTQ0jR45sX0Uym8106tSJU6dO4eXlxWOPPabzQwPc\nLS/4V9NnNps5d+4csbGx7f8vf4/bNoEXLlxg27ZtBAYGYrPZqKqqoqamBofDwY4dOwgKCsJut5Oa\nmkpwcDDDhg1jy5YteHt7M2TIEE3wh+zOvDp37kxVVRVtbW3079+fmJgYVq1aRa9evbDb7ZSUlFBb\nW8tnn31GW1sbEydO1NWeh+xuea1cuRKHw0FMTAxXr16ltraW4OBgunfvbnTpHunixYvk5OSwfft2\nbty4QVBQEF5eXvzyyy8MHDgQh8NBaWkp3t7exMfHA2CxWPD39yc8PBy73W7wCDzL3fLq06cPu3bt\nwmw209LSwtq1a9m0aROHDx8mMzOTsLAwo4fgUe4lr5KSkvbb4i9evEheXh4HDhxg/PjxhIaGGj0E\nj3JnXjdv3qRr164EBwfTq1cvnn76ad555x1GjBhBcHAwP/30EwUFBaxfv579+/czadIkIiMjjR6C\nR7mfvADq6+v58ccfiYuL03fXA3LLJrCoqIjly5djt9s5fPgwp0+fZs6cOZSWlvLhhx+SmpqKxWIh\nPz+fyMhIbt68yQcffEBCQgLz5s0zunyP80de3bp148iRI9TU1BAfH09JSQldu3alqqoKl8uF0+lk\nzJgxVFdXU1RURFJSEq+//roawIfsXvO6dOkSjz/+OAEBAVRWVpKYmIjNZjO6fI/0+eef4+/vz5w5\nczh06BAVFRVERERQX1+Pn58foaGheHl58cUXXzBlyhQAfHx8iI6O1peoAe4lL/jXMzGzZ89m9OjR\nhISEsGDBAjWABrjX+bVp0yaysrKwWq34+PiwePFiNYAGuDOvgwcPUllZybBhwwgJCcFsNtPY2EhR\nURHp6emEhISQlJSEzWYjOzubiIgIo8v3OPeS1/bt20lPTwcgJCSELl26kJSUZHDl7s+tmsDq6mps\nNhvbt2/n2Wef5ZlnnsFqtVJUVITdbicqKopr166xbNky+vfvz65duxg9ejRJSUmkp6eTkpJi9BA8\nyn/Lq0uXLpSXlxMbG8vQoUMpLS3FbDazcOFCvvvuO0aMGEFiYiJPPPGEJvhDdr957d69myFDhhAQ\nEMCQIUPUTDxkBQUFbNiwgZqaGurq6pg+fXr7qt6ZM2eor68nLi6OwsJC0tLSOHz4MP7+/gwaNEgX\nVgzwd/Iym80kJydjNpt1cvqQ/Z28/Pz8GDhwYPvmWfLw/FleISEhVFdXc/bs2fZzipSUFHJycoiI\niCAuLg6z2UxUVJSxA/AwfzevmJgYAHr27Glk+f8YbnMmcObMGV577TWampqoq6vj4MGDAERFRWEy\nmSgqKqK5uZnGxkY+/fRT5s6di8ViwWq1AuDn52dk+R7nz/KKjo7G5XKxd+9e+vfvT3JyMtHR0Sxd\nupTw8PD2Z8p8fX2NLN/j/J28wsLC2rfU9vHxMbJ8j7Ny5UrKysqYPn06NTU1FBYWtr88NzQ0lOTk\nZNra2hg6dCg9evRg4cKFbN68mYyMDGVlgAfJy2w2G1y953mQvPTd9fD9VV7du3dn2LBhnD9/HqfT\n2f6Z999/n+joaKNK9mjKq+Nwi7OB1tZWvv76a5qamli/fj1LlixhypQp9OzZk0OHDrUvGffu3Zvs\n7Gz27NnDxIkTSUtLM7p0j3S3vEJDQ2lra6OhoYHIyEg2btzI2LFjycjIMLp0j6S83E9jYyNZWVkk\nJCTwwgsvYLfbKSoqIiMjg/j4eIKCgrhx4wYhISEsWrSIa9eu0a1bN6PL9ljKy70oL/dyt7yCg4O5\nffs2AQEB7a/q0OZKxlFeHYdbrAS2tbUREBBAXl4eVVVVOJ1O8vLyuH37NikpKcyePZurV68SHBxM\nYmIi8+fPVwNooLvlNWvWLBobG7FYLPTu3Zu3335bDYWBlJd7aW1tJTU1lX79+gGwY8cORo0axbx5\n81ixYgWnT5+msrKShoYGbt26hY+Pj05QDaS83Ivyci/3ktfevXtxOp16p1wHoLw6FrdoAk0mE1lZ\nWYSHhzNhwgRWrlxJeHg4fn5+/P7778yfPx+73Y6Pjw9tbW1Gl+vx7pbXq6++qrw6EOXlXry9vRk+\nfDiBgYE0NTVx/PhxHn30UbKyshg+fDibN2/mxIkTvPnmm3plRwegvNyL8nIv95PXI488YnS5Hk95\ndSxuszFMQEAAALGxsezevRuXy8WECRM4ceIEY8aMYcqUKZhMJl016CCUl3tRXu6ptraWmzdvEhMT\nw/Lly7FarSxYsIC0tDS9P6kDUl7uRXm5F+XlXpSX8dzimcA7+fv789RTT7Fx40YmTJjA1KlTjS5J\n/oLyci/Ky73s37+f3Nxcjh07xqRJk5g4caLRJclfUF7uRXm5F+XlXpSX8bza3PT+rpaWFkwmk9Fl\nyD1SXu5FebmHgoICLl++zIwZM7SLpBtQXu5FebkX5eVelJfx3LYJFBHxdH/snCbuQXm5F+XlXpSX\ne1FexlMTKCIiIiIi4kHcYndQERERERER+b+hJlBERERERMSDqAkUERERERHxIGoCRUREREREPIia\nQBERkQewZMkStmzZ8qd/f+ONNzh37txDrEhEROSvqQkUERH5f7Rv3z60EbeIiHQkekWEiIjIfWhr\na+Pdd99lz5492O12WlpamDp1Kr/++iuVlZVcv34dq9XKRx99RGFhIWvWrCEiIoK8vDxqa2vJycmh\nubkZq9XKW2+9RXh4uNFDEhERD6OVQBERkftQXFzM8ePHKSoqYvXq1Zw9e5aWlhZOnTpFfn4+xcXF\nRERE8O233zJ79mzsdju5ublYLBaWLl3KqlWrKCws5OWXX2bZsmVGD0dERDyQj9EFiIiIuJOqqipS\nU1Px9fUlKCiIUaNGYTKZWLx4MV999RWnT5/m559/JiIi4t8+d+bMGWpra5k7d27775qamh52+SIi\nImoCRURE7oeXlxetra3tP/v4+OB0Opk5cyYvvfQS48aNw9vb+389B9ja2kpYWBhbt24FoKWlhStX\nrjzU2kVEREC3g4qIiNyXoUOHsnPnTlwuF9evX6e8vBwvLy9SUlJ47rnniIuLo6KigpaWFgBMJhMt\nLS3ExMRw/fp1Dhw4AEBBQQGLFi0ycigiIuKhtBIoIiJyH8aOHcuRI0fIyMjAZrMRGxtLc3Mz1dXV\nZGZm4uvri8PhoK6uDoDRo0cze/Zs1q1bx+rVq1mxYgW3b98mMDCQ9957z+DRiIiIJ9LuoCIiIiIi\nIh5Et4OKiIiIiIh4EDWBIiIiIiIiHkRNoIiIiIiIiAdREygiIiIiIuJB1ASKiIiIiIh4EDWBIiIi\nIiIiHkRNoIiIiIiIiAdREygiIiIiIuJB/geRleWh+l6ASwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f774d1ceeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.curve().returns().sum(axis=1).cumsum().plot(title='Cumulative returns for a diversified portfolio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Sharpe Ratio\n",
    "\n",
    "The accountCurve produces a number of statistics based on the returns series. The most important one is the Sharpe ratio, which is our measure of performance. It is the return we achieved, given the volatility we endured.\n",
    "\n",
    "$$\n",
    "\\text{Sharpe}\n",
    "=\n",
    "\\frac{\\text{Total Returns}}{\\text{Std dev of Returns}}\n",
    "$$\n",
    "\n",
    "In other words, it's the smoothness of returns. Smooth, predictible returns are the what we seek most, because we can use leverage to multiply them.\n",
    "\n",
    "A reasonable return for a trend following strategy is about 0.3 on single instrument, and about 0.7 on a diversified portfolio.\n",
    "\n",
    "We control the portfolio's volatility, so the expected return is:\n",
    "\n",
    "$$\n",
    "\\begin{eqnarray}\n",
    "\\text{Expected annual return}\n",
    "&=&\n",
    "\\text{Sharpe ratio } \\times \\text{Annual volatility target}\\\\\n",
    "&=& 0.7 \\times \\text{25%} \\\\\n",
    "&=& \\text{17.5% } \\pm \\text{25%}\n",
    "\\end{eqnarray}\n",
    "$$\n",
    "\n",
    "So this means we can expect to have some very good years, some years with a loss, and on average, return about 17.5% a year. In this regard, performance of trend following is **mean-reverting**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-22T10:51:18.831560",
     "start_time": "2017-07-22T09:50:16.787Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'annual_vol': '0.1254',\n",
       " 'avg_drawdown': -0.085356671645655913,\n",
       " 'avg_return_to_drawdown': 1.4015053194309313,\n",
       " 'calmar': 0.23483391351910637,\n",
       " 'cap': 500000,\n",
       " 'gross_sharpe': 0.93576890611289554,\n",
       " 'sharpe': 0.86767804316533059,\n",
       " 'sortino': 1.2150737071902162,\n",
       " 'time_in_drawdown': '0.9311',\n",
       " 'worst_drawdown': -0.50941462230740808}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.curve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the results for the Sharpe ratio above, we can see `gross_sharpe` and `sharpe`. `gross_sharpe` does not consider the impact of commissions and spreads (but does include spreads), so we can see the impact of our trading costs on overall performance.\n",
    "\n",
    "### How to improve Sharpe ratio\n",
    "\n",
    "The process for improving Sharpe ratio would be roughly:\n",
    "* Add more uncorrelated instruments. A diversified portfolio increases Sharpe and reduces volatility.\n",
    "* Add more uncorrelated rules/strategies. The more varied and profitable rules we have, the better.\n",
    "* Model costs better, and trade faster. Faster strategies usually have a higher Sharpe ratio, however, costs play a much larger role, so need to be accounted for carefully.\n",
    "\n",
    "### Limitations of Sharpe\n",
    "\n",
    "One problem with using the Sharpe ratio is that it penalises 'good' volatility. If our portfolio is flat, and then makes a sudden jump up, this will be great for us, but have a worse Sharpe ratio. One way to fix this is using the Sortino ratio, which only considers the downside volatility. This is something to investigate more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Portfolio\n",
    "\n",
    "A Portfolio is a collection of Instruments, defined by [config/portfolios.py](file/../../config/portfolios.py).\n",
    "\n",
    "Portfolio allows us to:\n",
    "* Calculate AccountCurve for all instruments combined\n",
    "* Work out ideal positions for all the instruments combined\n",
    "* Run bootstrapping of rules across an entire portfolio at once\n",
    "* Calculate the **frontier** - the ideal position we want to be in, and what we send to Interactive Brokers."
   ]
  }
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